Self-regulated learning significantly influences students’ academic behavior and performance. Traditional SRL assessment heavily relies on subjective and self-evaluation methods, which are susceptible to personal biases and cognitive limitations. In this study, we leverage mobile devices with GPS and sensors to collect self-reported and passive mobile sensing data from 211 college students over a year. Our aim is to conduct a passive assessment of self-regulated learning. To achieve this, we apply four deep learning models to analyze behavioral features associated with self-regulated learning using students’ life record data. Our findings demonstrate a precise assessment of student self-regulated learning, encompassing the following subscales. Environment structuring (MAE = 2.88, r = 0.54) represents participation in the organization and construction of the learning environment. Time management (MAE = 2.82, r = 0.57) represents planning study time and balancing study with other activities; Help seeking (MAE = 4.86, r = 0.53) represents the tendency and frequency of students to seek help during the learning process. Our study helps inspire new forms of education to assess self-regulated learning and paves the way for individualized interventions in future studies. • We develop and implement the iSense system for SRL assessment. • Collecting mobile sensing and self-reported data from 211 students over a year. • Establishing an SRL assessment model using mobile sensing data and deep learning. • We observe that SRL highly correlates with app usage patterns and sensing data. • Our study can achieve longitudinal monitoring and early intervention of SRL.

Understanding and identifying the nature of learner confusion is important for online learning platforms. In this study, we address this problem by analyzing forum posts from large-scale online courses. However, due to the large volume of comments and frequent interactions, confusion posts are often overlooked. Existing methods and models, while capable of detecting confusion, typically rely on linguistic features of posts and community factors (e.g. votes, views) but ignore personalized contexts, such as the specific causes and types of confusion. To address this problem, we create the first deep learning dataset focused on confusion types and develop a BERT-based network to model personalized features and identify confusion types. Considering the highly imbalanced distribution of different types of confusion, we further design a novel loss function that adaptively optimizes the training weights for each type. Our method’s effectiveness is confirmed through extensive experimentation.