Interpretable Cognitive Diagnosis with Neural Network for Intelligent Educational Systems

In intelligent education systems, one key issue is to discover students' proficiency level on specific knowledge concepts, which called cognitive diagnosis. Existing approaches usually mine the student exercising process by manually designed function, which is usually linear and not sufficient to capture complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex interactions between student's and exercise's factor vectors. The interpretability of factor vectors is guaranteed with the monotonicity assumption borrowed from educational psychology. We provide NeuralCDM model as an implementation example of the framework. Further, we explore the text content for improving NeuralCDM to show the extendability of NeuralCD, and demonstrate the generality of NeuralCD by proving how it covers some traditional diagnostic models. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.