Based on Graph-VAE Model to Predict Student's Score

TL;DR

This paper introduces a Graph-VAE model leveraging deep learning and graph neural networks to predict student scores, automatically extracting features and analyzing key influencing factors for early educational intervention.

Contribution

The paper presents a novel Graph-VAE model that automatically extracts features from educational data without manual filtering, outperforming traditional methods in score prediction and interpretability.

Findings

Model outperforms traditional solutions in accuracy.

Effectively captures student-curriculum relationships.

Visualized feature vectors show meaningful clustering.

Abstract

The OECD pointed out that the best way to keep students up to school is to intervene as early as possible [1]. Using education big data and deep learning to predict student's score provides new resources and perspectives for early intervention. Previous forecasting schemes often requires manual filter of features , a large amount of prior knowledge and expert knowledge. Deep learning can automatically extract features without manual intervention to achieve better predictive performance. In this paper, the graph neural network matrix filling model (Graph-VAE) based on deep learning can automatically extract features without a large amount of prior knowledge. The experiment proves that our model is better than the traditional solution in the student's score dataset, and it better describes the correlation and difference between the students and the curriculum, and dimensionality reducing the vector of coding result is visualized, the clustering effect is consistent with the real data distribution clustering. In addition, we use gradient-based attribution methods to analyze the key factors that influence performance prediction.