  Cognitive diagnosis is a fundamental issue in intelligent education, which
aims to discover the proficiency level of students on specific knowledge
concepts. Existing approaches usually mine linear interactions of student
exercising process by manual-designed function (e.g., logistic function), which
is not sufficient for capturing 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
exercising interactions, for getting both accurate and interpretable diagnosis
results. Specifically, we project students and exercises to factor vectors and
leverage multi neural layers for modeling their interactions, where the
monotonicity assumption is applied to ensure the interpretability of both
factors. Furthermore, we propose two implementations of NeuralCD by
specializing the required concepts of each exercise, i.e., the NeuralCDM with
traditional Q-matrix and the improved NeuralCDM+ exploring the rich text
content. Extensive experimental results on real-world datasets show the
effectiveness of NeuralCD framework with both accuracy and interpretability.
