Deep Knowledge Tracing Incorporating a Hypernetwork With Independent Student and Item Networks

Abstract

Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student and the difficulty of each item such as IRT. Nevertheless, its interpretability is inadequate compared to that of IRT because the ability parameter depends on each item. Deep-IRT implicitly assumes that items with the same skills are equivalent, which does not hold when item difficulties for the same skills differ greatly. For identical skills, items that are not equivalent hinder the interpretation of a student's ability estimate. To overcome those difficulties, this study proposes a novel Deep-IRT that models a student response to an item using two independent networks: 1) a student network and 2) an item network. The proposed Deep-IRT method learns student parameters and item parameters independently to avoid impairing the predictive accuracy. Moreover, we propose a novel hypernetwork architecture for the proposed Deep-IRT to balance both the current and the past data in the latent variable storing student's knowledge states. Results of experiments with six benchmark datasets demonstrate that the proposed method improves the prediction accuracy by about 2.0%, on average. In addition, experiments for the simulation dataset demonstrated that the proposed method provides a stronger correlation with true parameters than the earlier Deep-IRT method does at the $p< 0.5$ significance level.