A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models

Knowledge tracing (KT) plays a crucial role in predicting students’ future performance by analyzing their historical learning processes. Deep neural networks (DNNs) have shown great potential in solving the KT problem. However, there still exist some important challenges when applying deep learning techniques to model the KT process. The first challenge lies in modeling the individual question information. This is crucial because students’ knowledge acquisition on questions that share the same set of knowledge components (KCs) may vary significantly. However, due to the large question bank, the average number of interactions per question may not be sufficient. This limitation can potentially result in overfitting of the question embedding and inaccurate question knowledge acquisition state that relies on its corresponding question representation. Furthermore, there is a considerable portion of questions receiving relatively less interaction from students in comparison to the majority of questions. This can further increase the risk of overfitting and lower the accuracy of the obtained question knowledge acquisition state. The second challenge lies in interpreting the prediction results from existing deep learning-based KT models. In real-world applications, while it may not be necessary to have complete transparency and interpretability of the model parameters, it is crucial to present the model’s prediction results in a manner that teachers find interpretable. This makes teachers accept the rationale behind the prediction results and utilize them to design teaching activities and tailored learning strategies for students. However, the inherent black-box nature of deep learning techniques often poses a hurdle for teachers to fully embrace the model’s prediction results. To address these challenges, we propose a Question-centric Multi-experts Contrastive Learning framework for KT called Q-MCKT. This framework explicitly models students’ knowledge acquisition state at both the question and concept levels. It leverages the mixture of experts technique to capture a more robust and accurate knowledge acquisition state in both question and concept levels for prediction. Additionally, a fine-grained question-centric contrastive learning task is introduced to enhance the representations of less interactive questions and improve the accuracy of their corresponding question knowledge acquisition states. Moreover, Q-MCKT utilizes an item response theory-based prediction layer to generate interpretable prediction results based on the knowledge acquisition states obtained from the question and concept knowledge acquisition modules. We evaluate the proposed Q-MCKT framework on four public real-world educational datasets. The experimental results demonstrate that our approach outperforms a wide range of deep learning-based KT models in terms of prediction accuracy while maintaining better model interpretability. To ensure reproducibility, we have provided all the datasets and code on our website at https://github.com/rattlesnakey/Q-MCKT .