Lightweight GCN Predicts Classroom Grades with High Accuracy

Shenzhen, China, September 1, 2025

News Summary

A lightweight two-layer Graph Convolutional Network (GCN) can predict four levels of classroom performance with strong accuracy by combining student attributes and social interaction data. Tested on a cleaned dataset of 732 students and a social graph of 5,184 edges, the model uses a 16-feature input matrix and achieves AUC scores near 0.91–0.92 and an F1 around 87%. The approach outperforms GAT and GraphSAGE, and ablation shows social ties are critical. The study highlights interpretability via GNNExplainer, notes limits in scale and multimodality, and recommends ethical adaptation before wider deployment.

Lightweight graph AI model predicts classroom grades with high accuracy

Researchers have built a lightweight Graph Convolutional Network (GCN) that combines students’ personal data and their social interactions to predict classroom performance in four categories with strong accuracy. The study was published in Scientific Reports, volume 15, Article number 32044 (2025), DOI 10.1038/s41598-025-17903-4, and appeared online on 01 September 2025.

Top finding

The GCN model reached an area under the curve of approximately 0.91–0.92 and high F1 and precision scores in cross-validation. The model is aimed at making classroom grade evaluation more objective by adding structured social and online behavior data to traditional measures.

What was done

The team treated each student as a node in a graph and used different kinds of interactions as weighted edges. They combined data from school teaching systems, classroom observation notes, and online learning platforms to form a multi-source dataset. Platforms named in the work include the Smart Education Platform for Primary and Secondary Schools of Shenzhen and the xueleyun Teaching Platform.

Data and scope

• Data were gathered from 12 classes across four grade levels in two schools over two semesters.
• An initial pool of 802 student records was reduced to 732 valid records after cleaning (records with more than 30% missing values were removed).
• The final graph had 732 nodes and 5,184 edges, with an average node degree of about 14.16.
• Each node used a 16-dimensional feature vector (matrix X is 732 × 16) including personal and behavior indicators such as attendance, assignment timeliness, group cooperation counts, online activity, teacher and peer ratings, and more.

How labels were made

Performance labels were built by fusing mid-term and final scores (mid-term weight 0.4, final weight 0.6) and combining teacher, self and peer inputs into a fused score. The fused score was then turned into four classes: Excellent (≥90), Good (80–89), Qualified (70–79), and To be improved (<70).

Graph construction methods

The main edge-weight method combined three interaction indicators: observed cooperation in class, online interaction frequency, and peer ratings. These three were normalized and weighted by coefficients of 0.4, 0.3 and 0.3 respectively to build a weighted adjacency matrix. Alternative graph strategies tested included cosine similarity of interaction vectors, peer evaluation graph, Pearson similarity graph, and fully connected graph.

Model design and training

The model is a compact two-layer GCN with hidden sizes of 128 and 64, ReLU activation, and 0.5 dropout. Training used cross-entropy loss with L2 regularization (weight decay 0.0005) and the Adam optimizer (initial learning rate 0.01 with decay). The environment reported four NVIDIA A100 GPUs, dual Intel Xeon Gold processors, and 256 GB RAM, with software stacks including PyTorch 2.1, PyG 2.3, CUDA 12.1 and cuDNN 8.9.

Evaluation and results

• Data split was stratified: 70% train (512), 15% validation (110), 15% test (110), with 5-fold cross-validation and averages over five runs.
• The GCN achieved AUC ≈ 0.92 in a main figure, with cross-validation averages giving precision 88.52%, recall 86.47%, and F1 87.32%.
• GCN outperformed graph attention and GraphSAGE baselines (GAT AUC 0.88, GraphSAGE AUC 0.85) and beat traditional machine-learning and rule-based baselines by a large margin.
• Category-level results showed the model was best at spotting Excellent students (91% accuracy) and those needing improvement (86% accuracy). The middle category Qualified was more often confused with lower labels.
• Ablation tests confirmed the social graph is crucial: removing graph structure dropped AUC to 0.68 and reduced accuracy to 71%.

Interpretability and practical notes

The study used tools such as GNNExplainer and t-SNE to highlight which neighbors and features mattered most. For high-performing students, group cooperation, class participation, and assignment timeliness were often most influential. The authors present the two-layer GCN as a practical option for school settings because it balances performance and computation.

Ethics, access, funding and limits

The work was approved by a school ethics committee and all online behavior data were collected with authorization and privacy protections. The corresponding author can provide datasets on reasonable request by email: wushuying1234@126.com. The authors reported no competing interests. Funding came from local and provincial education projects and school support in Shenzhen.

Limitations include reliance on questionnaires and logs rather than richer multimodal signals, and potential scaling challenges for much larger networks. Future work suggested richer data types, distributed training, and stronger interpretability tools.

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Key features at a glance

Item	Detail
Article	Application of artificial intelligence graph convolutional network in classroom grade evaluation
Journal	Scientific Reports, vol. 15, Article 32044 (2025)
DOI	10.1038/s41598-025-17903-4
Published	01 September 2025
Final sample	732 student records; graph with 732 nodes and 5,184 edges
Input features	16 features (personal, classroom, online behavior)
Model	Lightweight two-layer GCN ([128,64]) with dropout and L2 regularization
Top performance	AUC ≈ 0.91–0.92; F1 ≈ 87.3%
Data access	Available on reasonable request: wushuying1234@126.com
Ethics	Approved by school ethics committee; written consent obtained

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