NeuralCD: A General Framework for Cognitive Diagnosis

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Abstract
Recent decades have witnessed a rapid growth of intelligent educational systems as well as an increasing demand for computer-aided educational technologies. One of the fundamental issues in intelligent education is cognitive diagnosis, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manually designed function (e.g., logistic function). However, the cognitive interactions between students and exercises is a complex process, and excessive simplifications would lead to under fitting and thus get inaccurate diagnostic results. Besides, the manually designed interaction functions are relatively inflexible and limits their extensibility. This consequently causes lack of consideration about useful non-numerical information in the cognitive process besides response logs. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework as well as several implemented models (a basic implementation NeuralCDM and three extensions), where we project students and exercises to factor vectors and incorporates neural networks to learn the complex exercising interactions. To ensure the interpretability of diagnostic results, which is essential for cognitive diagnosis, we apply an monotonicity assumption to our NeuralCD framework. Moreover, NeuralCD is a general framework and has good extensibility. We show the generality of NeuralCD through proving how it can cover some traditional models. Then, we demonstrate the extensibility of NeuralCD, which benefits future developments. On one hand, we demonstrate content-based extensions where we provide examples of exploring the rich contents of exercise texts (CNCD-Q and CNCD-F). On the other hand, we demonstrate a knowledge-association based extension to show that NeuralCD is flexible for structural adjustments so as to solve specific problems. For instance, we improve the diagnostic results on uncovered knowledge concepts of a student by extending NeuralCD with the knowledge associations consideration (KaNCD). Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
Funding Information
  • National Natural Science Foundation of China (61922073, U20A20229, 62106244)
  • Fundamental Research Funds for the Central Universities (WK2150110021)
  • Iflytek joint research program

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