# A Cognitive Diagnosis Model Using Convolutional Neural Networks to Predict Student Scores
## 2 Related Work
### Cognitive Diagnosis Based on Neural Networks  
In recent years, with the rapid development of neural networks, their application in cognitive diagnosis models has also made remarkable progress. The NCDM [6] is one of the earliest cognitive diagnosis models based on neural networks. It uses multiple neural layers to model the relationship between learners and exercises, and it applies the monotonicity hypothesis to ensure the interpretability of the model, which can capture students' mastery of different knowledge points and thus provide accurate results for cognitive diagnosis. The DIRT [21] model uses deep learning techniques to enhance the cognitive diagnostic capabilities of Item Response Theory (IRT), which utilizes neural networks to mine the semantic representation of problem text and is combined with the IRTmodel to improve diagnostic accuracy. The Deep-IRT [22] model is a synthesis of the Item Response Theory (IRT) model and knowledge tracking model. It is based on the deep neural network architecture of the Dynamic Key-Value Memory Network (DKVMN), which makes knowledge tracking based on deep learning explainable. The QNN [2] model proposes a neural network constrained by a Q matrix, which is used to determine the connections between neurons and the width and depth of the neural network, providing a realistic and implementable reference solution for classroom teaching evaluation and cold starts of personalized and adaptive evaluation systems. The RCD [23] model represents students, exercises and knowledge points as nodes in three local graphs, and it constructs a multi-layer attention network to aggregate the relationships between nodes in the graph and among graphs. This representation method can capture the interactive information between learners and exercises more comprehensively, and it improves the accuracy of cognitive diagnosis. The FK-CD [24] model introduced the importance of forgetting information and knowledge points on the basis of the cognitive diagnosis model. From the point of view of knowledge points, the model uses a neural network to obtain the degree of students' forgetting of knowledge points, and it predicts that according to the close connection between the test question and knowledge points. The C&RM [7] model expanded one-dimensional high-order ability features into independent ability features and effort features, and it set up a joint compensation mechanism between them. In addition, it also considered the difference between students' knowledge level and the importance of knowledge points as having an impact on answering questions, and it constructed the feature parameters of weak knowledge points. The KSCD model uses students' problem-solving records to learn the relationship between knowledge concepts, and obtains the embedded representation of knowledge point vectors based on neural networks. The above studies have effectively used neural networks to improve the accuracy of cognitive diagnosis. Therefore, this paper proposes a cognitive diagnosis model based on convolutional neural networks, which combines two multi-layer (general and special) one-dimensional convolutional neural networks to model the cognitive diagnosis process of students and the prediction process of final exam scores. At the same time, the attention mechanism is used to dig out the intra-layer relationships among students, questions and knowledge points, to improve the performance of the model.