A Cognitive Diagnosis Model Using Convolutional Neural Networks to Predict Student Scores

Smart education is an important direction of future educational development, aiming to improve the intelligence level of the existing digital education system and achieve the deep integration of information technology and mainstream education business. Most of the existing cognitive diagnostic models are trained and tested based on the answers to known questions, and the dependence on the trained questions leads to poor prediction results of the model for unknown questions. To solve that problem, this paper divides the questions of the dataset into the usual quizzes (known questions) and the final exam questions (unknown questions), which are used for training and testing, respectively. The cognitive diagnosis model based on a convolutional neural network (CNNCD) is proposed. Firstly, the attention mechanism is used to dig out the intra-layer relationships among students, questions and knowledge points, which alleviates the problem of insufficient information mining among students, questions and knowledge points. Secondly, two multi-layer (general and special) one-dimensional convolutional neural networks are combined to model the cognitive diagnosis of the process from students’ usual grades to students’ mastery of knowledge points. Finally, two multi-layer (general and special) one-dimensional convolutional neural networks are used to predict students’ final exam scores. Experiments on three public datasets and self-made BOIT datasets show that the proposed method is superior to other comparison models on these three evaluation indexes, indicating the feasibility and effectiveness of the proposed model.