NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features

Abstract

One of the most critical functions of modern intelligent teaching technology is cognitive diagnostics. Traditional cognitive diagnostic models (CDMs) usually use designed functions to deal with the linear interaction between students and exercises, but it is difficult to adequately deal with the complex relationship of non-linear interaction between students and exercises; moreover, existing cognitive diagnostic models often lack the integrated consideration of multiple features of exercises. To address these issues, this paper proposes a neural network cognitive diagnosis model (NeuralNCD) that incorporates multiple features. The model obtains more accurate diagnostic results by using neural networks to handle the nonlinear interaction between students and exercises. First, the student vector and the exercise vector are obtained through the Q-matrix; second, the multi-dimensional features of the exercises (e.g., difficulty, discrimination, guess and slip) are obtained using the neural network; finally, item response theory and a neural network are employed to characterize the interaction between the student and the exercise in order to determine the student’s cognitive state. At the same time, monotonicity assumptions and data preprocessing mechanisms are introduced into the neural network to improve the accuracy of the diagnostic results. Extensive experimental results on real world datasets present the effectiveness of NeuralNCD with regard to both accuracy and interpretability for diagnosing students’ cognitive states. The prediction accuracy (ACC), root mean square error (RMSE), and area under the curve (AUC) were 0.734, 0.425, and 0.776, respectively, which were about 2–10% higher than the related works in these evaluation metrics.