Exploring Student Representation For Neural Cognitive Diagnosis
This work addresses the need for better student representation in smart educational systems, though it appears incremental by building on existing vector-based methods.
The paper tackles the problem of representing students for cognitive diagnosis by proposing a method that incorporates hierarchical relations of knowledge concepts and student embeddings, resulting in improved effectiveness as shown in experiments.
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.