AuBoKT: an auxiliary boosted knowledge tracing model

Purpose This research aims to introduce a knowledge tracing (KT) method that evaluates students’ knowledge mastery state dynamically and precisely by analyzing their historical interaction data. Design/methodology/approach The proposed KT method is called Auxiliary Boosted Knowledge Tracing (AuBoKT). First, this paper presents a novel difficulty evaluation approach that takes into account individual abilities and the number of problem solvers, providing a more accurate estimation of exercise difficulty. In addition, this paper extracts various auxiliary features to mimic the learning process, enriching the information available for modeling students’ knowledge states. Moreover, this paper proposes a sequential neural network-based performance prediction model, which not only predicts students’ performance on given exercises but also implicitly models their knowledge state. Findings Extensive experiments on three public real-world data sets are conducted. The experimental results highlight the significance and effectiveness of each component in our approach. Originality/value This research addresses classical test theory’s limitations in exercise difficulty assessment by introducing a multi-concept fusion mechanism for comprehensive KT. This paper proposes AuBoKT, a deep learning framework leveraging auxiliary features to model fine-grained student interactions while dynamically integrating educational forgetting/learning theories for improved knowledge state tracking accuracy.