DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing

TL;DR

DASKT introduces a novel, computation-driven method that models students' dynamic affective states from behavioral data to enhance knowledge tracing accuracy and interpretability.

Contribution

The paper presents DASKT, a new approach that integrates affect simulation with knowledge tracing, improving performance prediction by modeling affective states from behavioral data.

Findings

DASKT outperforms existing KT methods in student performance prediction.

Affect simulation improves the interpretability of knowledge states.

Effective modeling of affective states enhances KT accuracy.

Abstract

Knowledge Tracing (KT) predicts future performance by modeling students' historical interactions, and understanding students' affective states can enhance the effectiveness of KT, thereby improving the quality of education. Although traditional KT values students' cognition and learning behaviors, efficient evaluation of students' affective states and their application in KT still require further exploration due to the non-affect-oriented nature of the data and budget constraints. To address this issue, we propose a computation-driven approach, Dynamic Affect Simulation Knowledge Tracing (DASKT), to explore the impact of various student affective states (such as frustration, concentration, boredom, and confusion) on their knowledge states. In this model, we first extract affective factors from students' non-affect-oriented behavioral data, then use clustering and spatiotemporal sequence modeling to accurately simulate students' dynamic affect changes when dealing with different problems. Subsequently, {\color{blue}we incorporate affect with time-series analysis to improve the model's ability to infer knowledge states over time and space.} Extensive experimental results on two public real-world educational datasets show that DASKT can achieve more reasonable knowledge states under the effect of students' affective states. Moreover, DASKT outperforms the most advanced KT methods in predicting student performance. Our research highlights a promising avenue for future KT studies, focusing on achieving high interpretability and accuracy.