A Plug-in for cognitive diagnosis method based on correlation representation under long-tailed distribution

Abstract

Cognitive diagnosis is a fundamental task in intelligence education, which aims to discover students’ proficiency for specific knowledge concepts. Existing cognitive diagnosis models are trained on the basis of sufficient student response records. In applications, however, these records usually follow a long-tailed distribution, i.e. there are only a few students with sufficient records, and a large number of students with a handful of records. The sparsity of records poses a challenge for cognitive diagnosis. To this end, a plug-in based on correlation representation is proposed to address cognitive diagnosis under long-tailed distribution, in which, the correlation representation between head students and tail students is learned to address the sparsity of long-tailed records. In particular, correlation representations are learned in view of both the cognitive state and the learning mode, which are learned based on the node representation and the subgraph representation, respectively. The correlation representation is then used as a plug-in to enhance the representation of long-tailed students and their related exercise and knowledge concepts. With the enhanced representations, the diagnostic performance of tail students is improved. Extensive experiments evaluate the improvement for diagnosis performance and the good compatibility of our plug-in component. Our code is available at https://github.com/joyce99/Wangmian.