RCKTE: Toward Global Optimal Causal Explanations for Deep Knowledge Tracing

Abstract

Deep-learning-based knowledge tracing (DLKT) models have achieved high predictive accuracy, but their opaque “black box” nature limits practical value: educators cannot trace why predictions are made and learners cannot obtain transparent feedback. Existing explainability techniques, which aim to explain why a model makes a particular prediction and provide the evidence for it, predominantly rely on correlational analyses, often yielding unfaithful or suboptimal explanations. To address this, we propose a post hoc reinforcement-learning-based causal deep knowledge tracing explainer (RCKTE). RCKTE operates through a structured workflow: First, it formulates the explanation task as a globally optimal subsequence screening problem, aiming to identify the most causally influential historical interactions from a student’s learning sequence. Then, a reinforcement learning agent, guided by a causal attribution reward and a dual-optimizer scheme, iteratively constructs the optimal subsequences by assessing the causal impact of each interaction. This process results in more faithful and concise explainable subsequences than those produced by correlation-based methods, achieving this within about 1 s to support real-time use. Finally, these explainable subsequences directly support actionable educational applications, including identifying a learner’s weak knowledge for targeted review, constructing personalized knowledge structure graphs for intervention tracking, and deriving group-level knowledge structures to guide curriculum design. Extensive experiments across multiple DLKT models and datasets confirm that RCKTE consistently outperforms existing post hoc methods in both the faithfulness and readability of explanations. By integrating causal attribution with reinforcement learning, RCKTE provides accurate, efficient, and educationally meaningful explanations that enhance the usability of DLKT in real learning environments.