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October 23, 2025Journal of Computational Methods in Sciences and Engineering

Quality analysis of college students’ innovation talent based on graph neural network

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Authors

LZLei ZhangXFXiaoxiao Fu

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Overview

Analysis demonstrates improved student performance predictions, identifying at-risk students using graph neural networks.

Key Points

  • Graph neural network models improve student performance prediction outcomes, enhancing identification of at-risk students.
  • Experimental results reveal substantial improvements with models leveraging similarity metrics and attention mechanisms.
  • A methodology based on graph neural networks captures complex data patterns in educational analytics.
  • This work suggests that graph neural networks may enable more personalized interventions in educational settings.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68fa1210f9f8b44535bfcea5https://doi.org/10.1177/14727978251391325
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Also Consider

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  1. 1Multimodal machine learning framework for predicting and enhancing higher education using reinforcement learning and graph neural networks2025
  2. 2Application of graph neural networks (GNNs) in learning situation prediction and educational resource recommendation algorithms2025
  3. 3GraphSeqNet: Enhancing Student Performance Prediction with Graph Neural Networks and Sequential Modelling2026
  4. 4Addressing Dropout through Personalization: A Graph Neural Network Approach to Modelling Learner Interactions2025
  5. 5Academic Trajectory Graphs for Temporal Modelling of Student Academic Progression2026