Principal Component Research of the Teaching Model Based on Multimodal Neural Network Algorithm.

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Machine Learning for Language Learning

Background:

• Enhancing English teaching quality is crucial amid educational reforms.
• Single neural networks struggle to capture the complexities of English education models.
• Existing models show limitations in describing the dynamic nature of teaching quality.

Purpose of the Study:

• To develop an advanced English teaching model using multimodal neural network algorithms.
• To address the limitations of single neural networks in modeling English education quality.
• To improve the accuracy and efficiency of English teaching quality assessment.

Main Methods:

• Constructed an English teaching model based on multimodal neural network algorithms.
• Utilized in-depth learning of contemporary English education trends and characteristics.
• Integrated neural network features with multimodal data for comprehensive modeling.

Main Results:

• The multimodal neural network model demonstrated a 76% higher convergence speed than single network models.
• Achieved a 79% reduction in the sum of squares of average error.
• Showcased average evaluation accuracy improvements of 13.99% and 6.42% over CNN and RBFNN models, respectively.
• Effectively realized global optimization capabilities.

Conclusions:

• The multimodal neural network model significantly enhances convergence speed and prediction accuracy in English teaching quality assessment.
• This approach provides a more effective and accurate method for modeling and improving English education quality.
• The study validates the efficacy of multimodal neural networks for educational modeling, offering a practical solution.