Augmenting Interpretable Knowledge Tracing by Ability Attribute and Attention Mechanism

TL;DR

This paper introduces a novel knowledge tracing model that incorporates ability attributes and attention mechanisms to improve prediction accuracy and interpretability in student learning performance modeling.

Contribution

The paper proposes a new model that accounts for changing student abilities and enhances interpretability using ability segmentation and attention weights.

Findings

Outperforms five existing knowledge tracing models in prediction accuracy

Effectively captures student ability variations over time

Provides interpretable inference paths for model predictions

Abstract

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.