  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.
