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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Application of artificial intelligence graph convolutional network in classroom grade evaluation.

¹Liyuan Foreign Language Primary School in Futian District, Shenzhen, 518000, China. wushuying1234@126.com.

Scientific Reports

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Summary

This study introduces a Graph Convolutional Network (GCN) model for classroom performance evaluation, improving objectivity and accuracy over traditional methods. The model effectively uses student social relationships for better educational assessment.

Keywords:

Classroom performance evaluation Educational data mining Graph convolutional network Intelligent education evaluation

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Area of Science:

Educational Technology
Artificial Intelligence
Data Science

Background:

Traditional classroom grading suffers from subjectivity and limited scope, failing to capture students' true learning status.
Existing educational data analysis methods often overlook the impact of social interactions on academic performance.

Purpose of the Study:

To develop an objective and accurate classroom performance evaluation model using Graph Convolutional Networks (GCN).
To leverage student social relationships within an interaction graph for enhanced educational assessment.
To provide a novel technical approach for intelligent and dynamic classroom grade assessment systems.

Main Methods:

Constructed an interaction relationship graph among students, integrating individual attributes and social connections.
Applied Graph Neural Network (GNN) techniques, specifically GCN, to analyze multi-source educational data.
Designed a GCN model architecture and training process tailored for educational assessment scenarios.

Main Results:

The proposed GCN model significantly outperformed traditional machine learning methods in a four-class classroom performance prediction task.
Ablation experiments confirmed the critical role of social relationship information in improving prediction accuracy.
Comparative analysis validated the effectiveness of different graph construction strategies.

Conclusions:

Graph Convolutional Networks offer a powerful tool for objective and accurate educational assessment.
Integrating social network analysis into GNN models enhances the prediction of student performance.
This research expands the application of GNN in educational data mining and paves the way for smarter assessment systems.