# Graph Neural Network for Senior High Student's Grade Prediction
## 1 Introduction  
Senior high school education (SHSE) has been considered as one of the most important education stages. SHSE concludes the k-12 education and also lays a foundation for the subsequent college education. High-quality SHSE should ensure the student processes corresponding knowledge well so that the graduate is more capable of integrating into society and entering into college education.  
When a senior high school graduate applies to college, his (or her) grade plays a critical role. In the United States, most colleges require the applicant to offer a grade point average (GPA) of senior high school, the American College Test (ACT), or the Scholastic Assessment Test (SAT). The grade accounts for a large proportion of college admission. In China, by the same token, college admission is mostly based on the grade of China's College Entrance Examination (CEE) [1]. Nearly every Chinese senior high graduate must take the CEE, and the CEE grade is the only criterion for college admission. Most other countries also take a similar policy that considers the grade as the main factor in college admission.  
Therefore, it is reasonable to utilize the grade of the senior high student as an indicator to instruct and ensure the effect of SHSE. This paper presents an artificial intelligence grade prediction model, in order to help the student and his (or her) teacher analyze and improve the study performance and teaching strategy. The proposed method utilizes the previous grades of the student to predict the grades in the subsequent examination. Based on the predicted grades, an adaptive study or teaching adjustment (like personalized feedback) can be made by the student or teacher themselves in, or even ahead of, time to ensure good education quality, before the grade decreases. When an examination has been carried out, the grades in the next examination can be predicted. Then, the predicted grades are utilizedto instruct and ensure the effect of education. A flow chart of this process is presented in Figure 1.  
Due to the complexity, nonlinearity, and unclear mechanism of the grade prediction problem, we propose a novel grade prediction method. Firstly, the grade data are represented by the graph structure. Then the multi-layer perceptron (MLP) is employed to learn (or fit) the relation of the graph structure data, to build the grade prediction model. The main idea is to extend the deep learning method applied in 1-dimension or 2-dimension data to graph structure data [2; 3; 4]. It is capable of learning (or fitting) the inner relationship among the data in the graph structure [5]. That makes the proposed model suitable for the prediction of senior high school students' grades (seen in Section 2).  
Essentially, the proposed model is a kind of supervised learning model [6]. A dataset is needed to train the neural network. In this paper, the dataset of senior high students in Ningbo Xiaoshi High School is utilized. The dataset contains every subject statistic (grade and rank) for the all students in each examination. The dataset and the personalized grade prediction model are both uploaded to the IEEE Dataport (URL: [[https://ieee-dataport.org/open-access/senior-high-student-dataset-ningbo-xiaoshi-high-school](https://ieee-dataport.org/open-access/senior-high-student-dataset-ningbo-xiaoshi-high-school)]([https://ieee-dataport.org/open-access/senior-high-student-dataset-ningbo-xiaoshi-high-school](https://ieee-dataport.org/open-access/senior-high-student-dataset-ningbo-xiaoshi-high-school)), accessed on 1 October 2021).