Behavior-Enhanced Knowledge Tracing Model in Intelligent Tutoring Systems

Abstract

Knowledge tracing endeavors to track shifts in students' knowledge states throughout the learning process by utilizing students' work records, enabling the prediction of their responses to the next question. As knowledge tracing advances, more information can be mined from the datasets to improve the model. However, most existing models only consider the correctness of the question and the concept as input, ignoring many other information gathered by intelligent tutoring systems (ITS). Therefore, this paper presents a Behavior-Enhanced Knowledge Tracing Model (BEKT). The Behavior-Enhanced Knowledge Tracing Model first selects multi-dimensional features by calculating the mutual information. The mutual information value shows that the most relevant features to the prediction target are student behavior information, including the count of hints, attempts made, and time taken to answer questions. Then, the model proposes a cross-feature fusion method to change students’ knowledge mastery state. Finally, a multi-layer perceptron is applied to integrate student behavior features to enhance knowledge state representation. Compared to existing models, BEKT more accurately captures changes in knowledge state, improving model performance.