Tracking Knowledge State Transitions in Cognitive Diagnosis for an Exercise-Exam Learning Model

Xinjie Sun, Qi Liu, Kai Zhang, Fei Wang, Shuanghong Shen

Abstract: Cognitive diagnosis (CD) models assess students’ knowledge state by analyzing test performance and identifying specific knowledge concepts mastered. However, traditional CD models often overlook dynamic changes in knowledge state during exercise and exam. To address this, we propose tracking knowledge state transitions in cognitive diagnosis (TKT-CD). The TKT-CD model introduces two key modules: the time-driven knowledge state transition (TDKT) module and the cognition-driven knowledge state transition (CDKT) module. TDKT captures temporal transitions in knowledge state during exercise, while CDKT analyzes relationships between knowledge concepts during exercise and exam to enhance reasoning accuracy. Combining neural networks with traditional static CD methods, TKT-CD resolves inconsistencies in knowledge state and provides more accurate representations of student interactions. Empirical studies show that TKT-CD significantly outperforms existing CD methods in predicting student performance, particularly in exercise and exam-based learning modes, offering a more effective tool for personalized education.

External IDs:dblp:journals/tii/SunLZWS25