2025 Volume 16 Issue 4 Pages 925-945
This study proposes an AI-driven learning behavior analysis and modeling framework aimed at supporting adaptive learning in English education. By incorporating multimedia technology, the system combines machine learning models with Internet of Things (IoT) technology to analyze students' behavioral performance online during the learning process and evaluate their learning outcomes. The core methodology of this framework includes the following steps: First, data preprocessing is conducted to optimize feature representation, and K-means clustering, along with Principal Component Analysis (PCA), is used to identify student behavior patterns and extract key features. Next, based on the clustering results, Mean Impact Value (MIV) analysis is applied to quantify feature importance and eliminate redundant information. Finally, multiple AI models, such as Bidirectional Long Short-Term Memory (BiLSTM), Temporal Convolutional Network (TCN), and Random Forest (RF), are integrated to capture temporal dependencies and spatial behavior patterns in learning tasks. We validated this method in four core English learning tasks: listening, speaking, reading, and writing. Experimental results show that the feature extraction strategy based on K-means-PCA-MIV optimization significantly improves the model's performance in complex evaluation scenarios. Among all the evaluation tasks, the Random Forest (RF) model performed the best, while BiLSTM demonstrated strong capability in modeling temporal data. Additionally, we found that writing tasks, due to their structured nature, are easier to assess, whereas speaking tasks present more challenges due to individual variability. This study provides new insights into intelligent assessment and personalized learning path design in English education and supports behavior data-driven dynamic learning intervention strategies, promoting the deep integration of complex communication science and multimedia technology in the education field.
