Rethinking Learner Modeling: A Feedback-Centric Cognitive Disentanglement Perspective

With the rise of web-based technologies, online tutoring platforms have emerged to provide personalized learning services by modeling learners' engagement behaviors, improving both convenience and efficiency in academic progress. Cognitive diagnosis has been always recognized a essential learner modeling task in personalized education, which aims to infer learners' mastery in specific knowledge concepts by mining and analyzing their practice behavior. However, most existing studies fail to explicitly disentangling the multiple interdependent factors that influence learner's response feedback during the problem-solving process, both in web-based environments and real-world contexts. To address this issue, we propose DISCD, a feedback-centric DIS entangled Cognitive Diagnosis framework for enhancing effective and interpretable learner modeling. Specifically, we first introduce a feedback-centric disentangled encoder grounded in variational inference to effectively characterize learners' cognitive traits by modeling their practice responses. To achieve this, we fully leverage the interaction matrix and the exercise-concept correlation matrix to extract implicit signals in the disentanglement process, employing three dedicated sub-encoders to efficiently and comprehensively capture these attributes. Next, we develop a multi-level cognitive coordination module to systematically model the disentangled cognitive factors, ensuring their seamless integration into the diagnosis decoding process. Finally, we design a cognitive interaction decoder to reconstruct and refine learners' engagement trajectories in exercises. Extensive experiments on four educational datasets validate the effectiveness of the proposed DISCD model in learner modeling for cognitive diagnosis.