With the global digital transformation of education, Knowledge Tracing, the core cognitive modeling module in intelligent tutoring systems, has become a key research focus in educational data mining. However, existing deep learning-based KT models are inherently black-box: while achieving high-accuracy answer performance prediction, they fail to clarify internal decision logic or translate outputs into actionable teaching guidance for students and teachers. Existing KT interpretability methods cannot simultaneously satisfy the core requirements of causality, actionability, pedagogical adaptability, and non-professional accessibility, severely limiting KT’s industrial deployment and compliant application of educational AI systems. To address these research gaps, we systematically establish the conceptual definition, mathematical formalization, and five core attributes of reverse attribution for KT sequence learning scenarios, and propose RA4KT, a model-agnostic post-hoc explainable framework specifically tailored to the sequential characteristics and pedagogical prior constraints of educational scenarios.