Research on multimodal English teaching model based on genetic algorithm

In recent years, English language teaching has undergone significant transformation due to the integration of technology and innovative teaching methodologies. Among the most promising advancements is the formation of multimodal teaching, which incorporates various forms of media. Despite the potential benefits associated with multimodal teaching, challenges remain in tailoring content to individual learning needs and optimizing the learning process for maximum effectiveness. To address these challenges, this research proposes a Multimodal English Teaching Framework (METF) to enhance the effectiveness and personalization of language learning. The framework integrates multiple teaching modalities, including text, audio, video, and interactive elements, to accommodate diverse learning preferences. It incorporates student interaction data (responses, time spent on tasks, and engagement levels), learner demographics, learning styles, and feedback records, thereby capturing the effectiveness of personalized content delivery. Pre-processing steps, including tokenization for text data, noise reduction for audio data, and normalization for resolution and interaction data, ensure data quality and consistency. Feature extraction techniques such as Convolutional Neural Networks (CNNs) for audio and video data and Term Frequency-Inverse Document Frequency (TF-IDF) for text data are applied to capture meaningful patterns. To enhance the fusion of multimodal data, feature-level fusion is implemented, integrating recovered characteristics from multiple modalities into a unified representation that supports more accurate decision-making. This research establishes the Resilient Genetic Algorithm (RGA) to enhance the adaptive learning process by ensuring robustness and avoiding premature convergence. Experimental results demonstrate the potential of this multimodal approach, achieving 94.2% accuracy, a convergence time of 45%, task engagement of 89.4%, and personalization effectiveness of 91.7%. These results indicate a high value of accuracy in multimodal English teaching. This research highlights the applicability of algorithms in optimizing complex, multimodal educational environments.