A Cognitive Diagnosis Model With Nonlinear Dependence Between Students and Exercises

Yuhong Zhang, Zhihao Lin, Chenyang Bu, Kui Yu, Xuegang Hu, Xindong Wu

Abstract: Cognitive diagnosis (CD) aims to discover students’ mastery of knowledge concepts through response logs and it is an important task in intelligence education. CD is generally performed with the advantage of students doing exercises, which is learned by linear interacting between student proficiency and exercise difficulty. However, existing methods represent the proficiency and difficulty in view of knowledge concepts independently, which is in coarse-granularity, leading to the indiscrimination for different combinations of students and exercises under the same concept. To this end, we propose a cognitive diagnosis method with nonlinear dependence between students and exercises (CDND), in which, more fine-grained information is captured to distinguish the different combinations of students and exercises. First, the nonlinear dependence is captured with a novel attention-like mechanism by interacting between students and exercises. Moreover, this nonlinear dependence is embedded as the interactive discrimination vector. Second, the interactive discrimination is used to adjust the advantage using a second-order interaction, which can strengthen the discrimination of the advantage under different combinations of students and exercises. Extensive experimental results on three real data sets validate our interactive discrimination has good compatibility with existing methods, and with it, our CDND achieves an obvious improvement in performance. Our code is available on https://github.com/joyce99/LinZhihao/tree/main/CDND-master.

External IDs:dblp:journals/tcss/ZhangLBYHW25