2025 Vol.8 Aug.N04

• Title: A method for predicting the quality of digital assisted teaching based on BP neural network
• Name: Xiaomei Yang
• Company: GuangXi Vocational Normal University, Nanning, 530000 China
• Abstract:

  Due to the wide and interwoven dimensions of digital assisted teaching quality data, the difficulty of data processing has increased, resulting in a lower coefficient of determination for prediction. To address the aforementioned issues, a digital assisted teaching quality prediction method based on BP neural network is proposed. Determine predictive indicators that can comprehensively reflect the quality of digital assisted teaching, including five dimensions: teaching ability, teaching attitude, teaching content setting, teaching method implementation, and teaching effectiveness. Build a BP neural network prediction model based on predictive indicators. The input layer receives teaching quality feature data and outputs prediction results after non-linear processing by the hidden layer. To improve the performance of the model, a selected training sample set is used for training, and the weights and thresholds are updated through steepest gradient descent. Particle swarm optimization algorithm is introduced to optimize the initial parameters, and multiple iterations are used for training to improve prediction accuracy and stability. The experimental results show that the research method can maintain a high coefficient of determination at different sample sizes, especially significantly better than the comparison method on large datasets, demonstrating stronger prediction accuracy and generalization ability.

  
  

• Keyword: BP neural network; Digital assisted teaching; Teaching quality; Predictive indicators; prediction model
• DOI: 10.12250/jpciams2025090812
• Citation form: Xiaomei Yang.A method for predicting the quality of digital assisted teaching based on BP neural network[J]. Computer Informatization and Mechanical System,2025,Vol.8,pp.52-55

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