# Graph Neural Network for Senior High Student's Grade Prediction
## 1 Introduction
### Related Work  
Since the mechanism of student performance prediction is not clear, most related studies are data-driven, and employ the education data mining (EDM) technology as the analysis tool [7; 8; 9]. The student performance prediction problem is related to two factors: data attributes and prediction methods [10]. Data attributes could be various, covering from the subject grade and demographics to dispositions [11; 12]. Prediction methods are mainly based on the EDM technologies, which contain linear regression [13], Naive Bayes algorithm [14], support vector machine [15], decision trees [16], and artificial neural networks [17]. Educational research has found that some specific data attributes (such as student's grades and attendance) are particularly relevant to predicting student performance. [PERSON] et al. [18] systematically analyze the effect of some specific attributes of student data utilized in predicting future student outcomes. These attributes are combined to generate a rule-based prediction model. For instance, a student should be predicted with low outcomes if he (or she) has either low grades or low attendance rates. Similarly, [PERSON] and [PERSON] [19] use the NBTree classifier, which combines the decision tree classifier and the Naive Bayes classifier, to create a set of rules to predict the student performance. [PERSON] et al. [20] build a collective student model to predict the student behavior in a procedural training environment. The point is that the collective student model is built based on the clustered past student action logs in the procedural training class. That makes the proposed collective model essentially a rule set-based model. A major drawback of such rule-based prediction models is the poor generalization ability, which means the models might work well for these specific situations (which satisfy the rule set well), but result in poor performance when the situation is not met. Figure 1: The flow chart to ensure the SHSE based on the student performance prediction. Recent research in data mining addresses the limitations of such rule set-based models by advocating automated learning methods. The regression method is employed in research [11; 12; 21]. In [11], the student engagement (data attribute) is included to build a regression-based predictor for undergraduate GPA prediction. [PERSON] et al. [12] combine the university student's self-regulated learning indicators and engagement with online learning logs to predict the student's academic performance. [PERSON] et al. [21] utilize the data collected from the Moodle learning management system to predict and compare the student performance of 17 blended courses by a multiple linear regression method. [PERSON] et al. [22] utilize the Naive Bayes algorithm to predict the performances of students (fail or pass) in the final examination of the university-level distance learning, with the student's demographic characteristics and marks on a few written assignments. In [23], five data mining methods (random forest, AdaBoost, logistic regression, support vector machine, and decision trees) are inspected in the senior high student outcome prediction of two U.S. school districts. [PERSON] et al. [24] propose a combined method to predict the dropout rates of students in e-learning courses (computer networks and communications, web design). Student logs extracted from the learning management system that hosts the e-learning courses are utilized. The prediction model is composed of three methods (feed-forward neural network, support vector machine, and probabilistic ensemble simplified fuzzy ARTMAP) in a voting mechanism. If one of the three methods votes for the dropout prediction, the student is predicted as dropout-prone. With the booming development of artificial intelligence [25], the artificial neural network is employed in education analysis and prediction [26], due to its capacity of learning (or fitting) the nonlinear relation. [PERSON] et al.