Construction and optimization of English learning behavior prediction model based on F-PID algorithm

With the rapid development of artificial intelligence technology, personalized learning has become a research hotspot in the field of education. In the context of globalization, effectively predicting English learning behaviors is of great significance for enhancing learning outcomes. This study focuses on this by constructing a prediction model for English learning behaviors based on the F-PID algorithm and optimizing its performance through experiments. The research addresses the limitations of linearity and symmetry in traditional algorithms by introducing new fuzzy rules and membership functions. Additionally, to solve the noise problem in derivative terms, a filtering mechanism is implemented in the algorithm, which enhances the algorithm's nonlinear processing capabilities and prediction accuracy. Through an in-depth analysis of the F-PID algorithm's complexity in time and space, the study clarifies the impact of factors such as the number of fuzzy rules and PID operations on the algorithm and demonstrates optimization techniques for managing complexity. Experimental validation shows that this algorithm integrates fuzzy logic with F-PID control theory, extracts key features like learning time and frequency from large-scale data, and completes model training through cross-validation and parameter tuning. The optimized F-PID model achieves a prediction accuracy of 85.6%, an improvement of 12.3 percentage points over traditional models, and demonstrates exceptional robustness and adaptability when handling data from students with different learning styles and levels. The mean absolute errors in predicting learning progress and scores are only 3.2% and 4.5%, respectively, far surpassing similar models. This achievement opens up new pathways for predicting English learning behaviors, helping English educators better understand student learning behaviors to adjust teaching strategies, and also aiding English learning platforms in personalizing user learning experiences, providing a solid theoretical foundation for personalized education, and showcasing unique academic value and application potential.