# A Cognitive Diagnosis Model Using Convolutional Neural Networks to Predict Student Scores
## 1 Introduction  
In recent years, with the rapid development of information technology such as the Internet era and computers, the modern education system has undergone fundamental changes, and the traditional education mode has gradually transformed into a new intelligent and personalized modern education mode [1]. Cognitive diagnosis is an important part of modern intelligent education [2], and its model is also an important research direction in the field of educational evaluation. It mainly evaluates and diagnoses students' mastery of specific knowledge points or cognitive skills through the response data of students. These models not only pay attention to the overall ability level of students but also deeply analyze the specific performance of students in various cognitive attributes (such as knowledge points, skills, etc. ), to provide teachers with more accurate teaching feedback and then provide personalized education resource recommendation for students [1]. At present, there are many kinds of cognitive diagnostic models, which can be roughly divided into two categories, simple models and complex models. The Item Response Theory (IRT [3]) and the Deterministic Inputs, Noisy And gate model (DINA [4]) are representative of simple models. Item Response Theory (IRT) is a modern psychometric theory that analyzes and evaluates test scores, questionnaire survey data, etc. The model is modeled through the potential learning ability of students, combined with the difficulty and distinction degree of questions, etc. The model is widely used, but has some limitations. Deterministic Inputs, Noisy And gate model (DINA) is also a widely used cognitive diagnostic model, which mainly describes the students' answering process by slipping and guessing parameters and the knowledge point investigation matrix, and then evaluates the students' knowledge mastery ability. However, the model is too simple, uses single, large parameters, is easy to overfit, and has a low prediction accuracy. Complex models include the Fuzzy Cognitive Diagnosis Framework (FuzzyCDF [5]), Neural Cognitive Diagnosis Model (NCDM [6]) and Cognitive and Response Model (C&RM [7]). The Fuzzy Cognitive Diagnostic Framework (FuzzyCDF) is a four-tier (i.e., latent trait, skill proficiency, problem mastery and problem score) generative model designed to capture the relationship between a student's knowledge state and their performance on objective and subjective questions. Although this allows for the joint investigation of multiple knowledge points, the importance placed on the knowledge points is insufficient. The Neural Cognitive Diagnosis Model (NCDM) combines neural networks to learn the complex interactions between students and questions, while drawing on the monotony hypothesis of pedagogy theory to ensure the interpretability of vectors, but the evaluation results are not convincing and prone to overfitting. The Cognitive and Response Model (C&RM) combines two high-order feature parameters to accurately model students' knowledge levels by setting the mutual compensation mechanism of the ability feature and effort feature. At the same time, the characteristic parameters of weak knowledge points are constructed to comprehensively consider the influence of students' knowledge level and different knowledge points on the answered questions, so as to further improve the interpretation and prediction accuracy of the model. However, the reliability of the student effort characteristic parameters is not high, and the initial parameters need to be optimized. The previous cognitive diagnosis process diagram is shown in Figure 1. In the selection of questions, the same proportion of questions are selected from each student's questions for training, and the rest are tested (for example, student 1 selected questions e1, e2 and e3 for cognitive diagnosis training, and then tested e4 and e5; Student 2 selected questions e1, e4 and e5 for cognitive diagnostic training, and then used e2 and e3 for the test).