A survey of explainable knowledge tracing.

With the long-term accumulation of high-quality educational data, artificial intelligence (AI) has shown excellent performance in knowledge tracing (KT). However, due to the lack of interpretability and transparency of some algorithms, this approach will result in reduced stakeholder trust and a decreased acceptance of intelligent decisions. Therefore, algorithms need to achieve high accuracy, and users need to understand the internal operating mechanism and provide reliable explanations for decisions. This paper thoroughly analyzes the interpretability of KT algorithms. First, the concepts and common methods of explainable artificial intelligence (xAI) and knowledge tracing are introduced. Next, explainable knowledge tracing (xKT) models are classified into two categories: transparent models and "black box" models. Then, the interpretable methods used are reviewed from three stages: ante-hoc interpretable methods, post-hoc interpretable methods, and other dimensions. It is worth noting that current evaluation methods for xKT are lacking. Hence, contrast and deletion experiments are conducted to explain the prediction results of the deep knowledge tracing model on the ASSISTment2009 by using three xAI methods. Moreover, this paper offers some insights into evaluation methods from the perspective of educational stakeholders. This paper provides a detailed and comprehensive review of the research on explainable knowledge tracing, aiming to offer some basis and inspiration for researchers interested in the interpretability of knowledge tracing. [ABSTRACT FROM AUTHOR]