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.

Recent advances in oracle bone inscriptions (OBI) classification have explored various strategies such as zero-shot learning, augmentation, and complex convolution architectures. These strategies ultimately represent samples as global feature vectors in various forms but often fail to effectively capture the inherent multi-scale and region-specific features of OBI. The reliance on global feature vectors ignores the subtle differences between different scales and the uneven importance of different regions in the inscriptions. To address this issue, we propose a dual-pyramid attention collaborative network that enables classification models to learn OBI multi-scale attention. The dual-pyramid structure covers the convolutional and skeletonized features of pyramids. The spatial collaborative attention between level pairs corrects the bias produced by the pre-trained convolutional feature extractor. Experiments show that our model reduces training parameters by an average of 38% and improves accuracy by an average of 3.89% compared to state-of-the-art models.

In knowledge distillation, the performance of teacher models often serves as an upper limit for student models. For a long time, deeper and more accurate supervised learning algorithms have been the first choice for teacher models in image classification tasks where unsupervised models typically underperform. Therefore, the value of unsupervised teachers for distillation has not been explored. This paper demonstrates an effective path to distill unsupervised teacher clustering knowledge to students. Unlike traditional distillation methods where the teacher model guides the student, we use an annealing strategy to progressively decrease the teacher model’s influence and increase the student model’s own contribution to the distillation loss. Experiments show that, in transferring unsupervised knowledge, the proposed method (AD) improves the student’s accuracy by an average of 9.38% compared to the state-of-the-art. In transferring supervised knowledge, the proposed method performs slightly worse than the state-of-the-art but converges faster during the early epochs.

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.