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Education

Neural Network-Based Model for Cultivating Applied Talents in Industry-Education Integration

Strategic Insights

— 1 min read

Researchers at Medical College have proposed a neural network-based model to integrate industry and education, combining Transformer and Residual Network (ResNet) architectures with a data augmentation strategy. The study aims to address the demand for skilled applied professionals and evaluate different teaching methods based on academic performance, practical skills, innovation, and professional competency alignment.

Key Takeaways:

  • The proposed model achieved a Log Loss of 0.106, a Mean Absolute Error (MAE) of 0.057, an Area Under the Precision-Recall Curve (AUC-PR) of 0.896, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.975 with small datasets (1,000 data points).
  • The model's performance declined with larger datasets, where Log Loss increased to 0.160, MAE to 0.071, AUC-PR decreased to 0.87, and AUC-ROC to 0.961.
  • Compared to ResNet, the optimized model showed better performance with small datasets, while ResNet exhibited greater stability and lower Log Loss with larger datasets.
  • The study found that practical teaching had the strongest impact on academic performance and practical skills, while Project-Based Learning (PBL) and Flipped Classroom were more effective in fostering innovation and academic research skills.
  • The research concluded that the findings may help refine talent cultivation models and teaching assessment methods.

Statistics:

  • Log Loss: 0.106 with small datasets, 0.160 with large datasets
  • Mean Absolute Error (MAE): 0.057 with small datasets, 0.071 with large datasets
  • Area Under the Precision-Recall Curve (AUC-PR): 0.896 with small datasets, 0.87 with large datasets
  • Area Under the Receiver Operating Characteristic Curve (AUC-ROC): 0.975 with small datasets, 0.961 with large datasets

Sources:

  • A neural network-based model for cultivating applied talents in the context of industry-education integration. Discover Artificial Intelligence, 2025,5(1):1-14. (DOI: 10.1007/s44163-025-00302-z)
  • NewsRx. Findings from Medical College Provide New Insights into Artificial Intelligence (A neural network-based model for cultivating applied talents in the context of industry-education integration). Education Letter. September 3, 2025; p 122.

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