Graph Neural Network for Senior High Student’s Grade Prediction

Abstract

Senior high school education (SHSE) forms a connecting link between the preceding junior high school education and the following college education. Through SHSE, a student not only completes k-12 education, but also lays a foundation for subsequent higher education. The grade of the student in SHSE plays a critical role in college application and admission. Therefore, utilizing the grade of the student as an indicator is a reasonable method to instruct and ensure the effect of SHSE. However, due to the complexity and nonlinearity of the grade prediction problem, it is hard to predict the grade accurately. In this paper, a novel grade prediction model aiming to handle the complexity and nonlinearity is proposed to accurately predict the grade of the senior high student. To deal with the complexity, a graph structure is employed to represent the students’ grades in all subjects. To handle the nonlinearity, the multi-layer perceptron (MLP) is used to learn (or fit) the inner relation of the subject grades. The proposed grade prediction model based on graph neural network is tested on the dataset of Ningbo Xiaoshi High School. The results show that the proposed method performs well in the prediction of senior high school student grades.

Achievement Performance Prediction Model Based on Deep Neural Network of Student Similarity

“…Yao et al [7] collected and constructed a real dataset of students' campus online behavior and achievement data, and proposed an end-to-end two-layer self-attentive network, which introduces a cascading self-attention mechanism to extract students' local online behavioral features for each day and global online behavioral features for a long period of time, respectively, and better solves the problem of long behavioral sequence modeling. Yu et al [8] used graph neural networks to represent students' performance in various subjects and utilized multilayer perceptrons to learn the intrinsic relationship between subject performances. Li et al [9] proposed an end-to-end deep learning model that automatically extracts features from students' heterogeneous behavioral data from multiple sources to predict academic performance.…”