Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning

• URL: http://arxiv.org/abs/2312.10045v2
• Date: Fri, 31 May 2024 14:19:03 GMT
• Title: Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
• Authors: Jiajun Cui, Minghe Yu, Bo Jiang, Aimin Zhou, Jianyong Wang, Wei Zhang,
• Abstract summary: Knowledge tracing plays a crucial role in computer-aided education and intelligent tutoring systems. Current approaches have explored psychological influences to achieve more explainable predictions. We propose RCKT, a novel response influence-based counterfactual knowledge tracing framework.
• Score: 10.80973695116047
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records. While existing deep learning knowledge tracing (DLKT) methods have significantly improved prediction accuracy and achieved state-of-the-art results, they often suffer from a lack of interpretability. To address this limitation, current approaches have explored incorporating psychological influences to achieve more explainable predictions, but they tend to overlook the potential influences of historical responses. In fact, understanding how models make predictions based on response influences can enhance the transparency and trustworthiness of the knowledge tracing process, presenting an opportunity for a new paradigm of interpretable KT. However, measuring unobservable response influences is challenging. In this paper, we resort to counterfactual reasoning that intervenes in each response to answer \textit{what if a student had answered a question incorrectly that he/she actually answered correctly, and vice versa}. Based on this, we propose RCKT, a novel response influence-based counterfactual knowledge tracing framework. RCKT generates response influences by comparing prediction outcomes from factual sequences and constructed counterfactual sequences after interventions. Additionally, we introduce maximization and inference techniques to leverage accumulated influences from different past responses, further improving the model's performance and credibility. Extensive experimental results demonstrate that our RCKT method outperforms state-of-the-art knowledge tracing methods on four datasets against six baselines, and provides credible interpretations of response influences.

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