Knowledge tracing (KT) models students' knowledge states and predicts their future performance based on their historical interaction data. However, attention based KT models struggle to accurately capture diverse forgetting behaviors in ever-growing interaction sequences. First, existing models use uniform time decay matrices, conflating forgetting representations with problem relevance. Second, the fixed-length window prediction paradigm fails to model continuous forgetting processes in expanding sequences. To address these challenges, this paper introduces LefoKT, a unified architecture that enhances attention based KT models by incorporating proposed relative forgetting attention. LefoKT improves forgetting modeling through relative forgetting attention to decouple forgetting patterns from problem relevance. It also enhances attention based KT models' length extrapolation capability for capturing continuous forgetting processes in ever-growing interaction sequences. Extensive experimental results on three datasets validate the effectiveness of LefoKT.

Knowledge tracing (KT) is a prediction task that aims to predict students’ future performance based on their past learning data. The rapid progress in attention mechanisms has led to the emergence of various high-performing attention based KT models. However, in online or personalized education settings, students’ varying learning paths result in different lengths of student interaction sequences, which poses a significant challenge for attention based KT models as their context window sizes are fixed during both training and prediction stages. We refer to this as the length extrapolation of KT model. In this paper, we propose extraKT to facilitate better extrapolation that learn from student interactions with a short context window and continue to perform well across various longer context window sizes at prediction stage. Specifically, we negatively bias attention scores with linearly decreasing penalties that are proportional to query-key distance, which efficiently represents short-term forgetting characteristics of student knowledge states. We conduct comprehensive and rigorous experiments on three real-world educational datasets. The results show that our extraKT model exhibits robust length extrapolation capability and outperforms state-of-the-art baseline models in terms of AUC and accuracy. To encourage reproducible research, we merge our data and code to the publicly available pyKT benchmark at https://github.com/pykt-team/pykt-toolkit.

Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.