Process Data and Speed Prediction for Fine-grained Knowledge Tracing
Xin Wang, Mengxiao Zhu, Zihang Chen, Shuanghong Shen · 2025
With the widespread adoption of online learning, providing learners with appropriate and personalized learning services (such as tailored learning resources or learning paths) is crucial when facing a vast array of learning materials. Achieving this demand relies on accurately tracing the learner’s knowledge state, a process known as knowledge tracing (KT). The goal of KT is to dynamically monitor the knowledge state of learners based on their historical exercise data and predict their future performance. While the process data reflects the learner’s knowledge state, the existing KT models have used this data relatively simply and directly, overlooking the importance of context-aware relationships. This underutilization of process data limits the models’ ability to fully capture the nuances of the learner’s knowledge state. To address this limitation, this study proposes Process data and Speed prediction for Fine-grained Knowledge Tracing (PSFKT). The PSFKT model integrates the context-aware representation of process data and adopts a multi-task prediction paradigm to improve the prediction of learners’ responses through the auxiliary task of predicting their answer speed. Additionally, the model considers the forgetting factor of time and knowledge concepts interaction to simulate the dynamic nature of human knowledge acquisition and retention. Through comparative and ablation experiments, the study demonstrates the effectiveness of the PSFKT model in accurately tracing learners’ knowledge states and predicting their future performance.