Augmenting Interpretable Knowledge Tracing by Ability Attribute and Attention Mechanism
This work addresses the need for more interpretable and adaptive student performance prediction in online education, though it appears incremental as it builds on existing knowledge tracing methods with added features.
The paper tackles the problem of modeling students' changing abilities and improving interpretability in knowledge tracing by proposing a model that segments interaction sequences, groups students by ability, and uses attention weights to quantify exercise-skill relevance. The results show that the model outperforms five existing knowledge tracing models on real online education datasets.
Knowledge tracing aims to model students' past answer sequences to track the change in their knowledge acquisition during exercise activities and to predict their future learning performance. Most existing approaches ignore the fact that students' abilities are constantly changing or vary between individuals, and lack the interpretability of model predictions. To this end, in this paper, we propose a novel model based on ability attributes and attention mechanism. We first segment the interaction sequences and captures students' ability attributes, then dynamically assign students to groups with similar abilities, and quantify the relevance of the exercises to the skill by calculating the attention weights between the exercises and the skill to enhance the interpretability of the model. We conducted extensive experiments and evaluate real online education datasets. The results confirm that the proposed model is better at predicting performance than five well-known representative knowledge tracing models, and the model prediction results are explained through an inference path.