# Graph Neural Network for Senior High Student's Grade Prediction
## 1 Introduction
### Related Work
[27] employ the multi-layer perceptron (MLP) to predict the student's academic performance. The unsupervised learning algorithm, named sparse auto-encoder, is used to pre-train the MLP. Then, the back-propagation method is utilized to fine-tune the MLP. [PERSON] et al. [28] develop a novel machine learning method for predicting student performance in degree programs, considering the student's evolving progress. The machine learning method contains the ensemble-based progressive prediction part (to predict the performance) and the course relevance discovering part (to consider students' evolving progress). In the first part, a bi-layered structure comprising multiple base predictors and a cascade of ensemble predictors is applied. In the second part, a data-driven approach based on latent factor models and probabilistic matrix factorization is employed. In [29], MLP is utilized to predict the career strand of the senior high school student (such as general academic strand, sciences, technology, engineering, and mathematics strand, accountancy, business, and management strand). The data of 293 students in 11 th grade are considered. [PERSON] et al. [30] use a deep support vector machine to predict student performances with school and family tutoring. [PERSON] [31] predicts the applicant's academic performance to support decision-making in the university admission system. Four methods (artificial neural network, decision tree, support vector machine, and Naive Bayes) are compared, and the artificial neural network outperforms. As a summary, the recent works are presented in Table 1.  
The analysis of the literature reveals that the studies of student performance prediction and analysis evolve from the rule set-based predicting method to the data mining method and the artificial intelligence method, due to the more powerful feature extracting and representing abilities provided by the artificial intelligence method. These abilities make the artificial intelligence method more capable of learning (or fitting) the nonlinear model. As described in [31], the artificial neural network outperforms the other methods (decision tree, support vector machine, and Naive Bayes) in the academic performance prediction. However, most related published studies that address the performance prediction problem focus on building a universal model which applies to all students. For instance, [PERSON] [31] considers a dataset that contains 2039 students' information, and uses the artificial neural network to build a unified model to predict each student's academic performance, in order to give some suggestions for the university admission system. This leads to a drawback that the individual features, such as the specific learning style,variation tendency with time, etc., are not considered, which might reduce the prediction performance. Besides, although most neural network based student performance prediction models achieve relatively good performances, they only treat the model as an end-to-end learning process without investigating the relationship between the data, causing the models to lack interoperability.