Dual Autoencoder Enhanced Subgraph Pattern Mining for Cognitive Diagnosis

In adaptive learning, Cognitive diagnosis aims to discover students' knowledge state on different knowledge con-cepts and predict their future performance. Most previous methods consider more on students' own answering history and rarely model the the impact brought by students with similar answering behaviors explicitly. This collaborative information among students is helpful for students who lack sufficient historical logs. In this paper, we propose a new cognitive diagnosis method called Dual Autoencoder Enhanced Subgraph Pattern Mining(DASPM) for Cognitive Diagnosis, which incorporates collaborative information among students into the cognitive di-agnosis process to obtain more accurate predictions. Specifically, we use a graph neural network to capture collaborative pattern on the student-exercise bipartite graph. In order to filter out the interference of irrelevant information, we design a sub graph extraction algorithm that separates local parts around the target student-exercise pair from global graph based on the correlation between exercises. In addition, we utilize a dual autoencoder module to encode students and exercises to enhance the initial representation of nodes in the sub graph. Extensive experiments on multiple datasets show the effectiveness of our proposed method.