Exploring Student Representation For Neural Cognitive Diagnosis

TL;DR

This paper introduces a novel student representation method for cognitive diagnosis that captures hierarchical relations among knowledge concepts and student profiles, improving the accuracy of proficiency estimation.

Contribution

It proposes a hierarchical student representation combining concept relations and student embeddings, addressing limitations of previous knowledge proficiency vectors.

Findings

Effective in capturing concept relations and student profiles

Improves proficiency estimation accuracy

Validated through experiments

Abstract

Cognitive diagnosis, the goal of which is to obtain the proficiency level of students on specific knowledge concepts, is an fundamental task in smart educational systems. Previous works usually represent each student as a trainable knowledge proficiency vector, which cannot capture the relations of concepts and the basic profile(e.g. memory or comprehension) of students. In this paper, we propose a method of student representation with the exploration of the hierarchical relations of knowledge concepts and student embedding. Specifically, since the proficiency on parent knowledge concepts reflects the correlation between knowledge concepts, we get the first knowledge proficiency with a parent-child concepts projection layer. In addition, a low-dimension dense vector is adopted as the embedding of each student, and obtain the second knowledge proficiency with a full connection layer. Then, we combine the two proficiency vector above to get the final representation of students. Experiments show the effectiveness of proposed representation method.