Explore Bayesian analysis in Cognitive-aware Key-Value Memory Networks for knowledge tracing in online learning

Abstract

Knowledge Tracing is a crucial aspect of personalized learning that aims to track the evolving knowledge states of students with respect to one or more concepts. However, existing literature either models knowledge state for each predefined concept separately, potentially overlooking the underlying interrelationships between concepts, or lacks the capability to uncover long-term dependencies within a historical sequence of questions. Furthermore, they have limitations in precisely identifying the mastery level of individual learners for specific concepts. In order to address these problems, we propose a novel deep learning-based knowledge tracing model, named Bayesian Cognitive-aware Key-Value Memory Network (BCKVMN). BCKVMN combines the strengths of Dynamic Key-Value Memory Network (DKVMN), Long Short-Term Memory (LSTM), embedding layer, and Bayesian theory. To capture long-term dependencies in question sequences and contextual information, we extend DKVMN by incorporating a standard LSTM module that integrates cognitive processes observed in human learning. Additionally, we employ an embedding layer to uncover the influence of question difficulty on final predictions. Furthermore, we introduce Bayesian theory to enhance parameter interpretation for the model. To investigate complex representation of human learning, our approach considers not only three distinct learning states during the learning process but also the relationships between underlying concepts and students’ mastery levels for each concept. Experimental evaluations are conducted on the three real-world datasets Algebra200, ASSISTMENTS2009 and ASSISTMENTS2017. The results have demonstrated that the proposed model outperforms state-of-the-art methods (achieving nearly 7% improvement in AUC in some cases) while achieving a better balance between performance and interpretability.