AI and Biotechnology-Driven Learning Behavior Analysis and Assessment in English Education

Xinhua Wang
Zhumadian Preschool Education College, China

Abstract:

With the continuous advancement of information technology and biotechnology, learning behavior analysis and personalized education in English language teaching have gradually become an important direction for educational reform. This paper proposes a new learning behavior analysis model combining biotechnology and artificial intelligence, aimed at evaluating learning performance in English education. The method collects students’ learning behaviors through the Internet of Things (IoT) and provides feedback on students’ learning outcomes through dynamic monitoring and analysis, thus supporting educational decision-making with data. Specifically, K-means clustering and Principal Component Analysis (PCA) are used to identify learning behavior patterns and extract key features that influence learning outcomes. Based on this, Mean Impact Value (MIV) analysis quantifies the features, selecting the most representative ones and eliminating redundant data. Subsequently, a learning effectiveness evaluation model is established, incorporating the principles of neurofeedback from biotechnology. Experimental results demonstrate that the clustering and AI strategies based on K-means–PCA–MIV optimization significantly enhance the model’s evaluation performance. In the evaluation of four core English learning tasks—listening, speaking, reading, and writing—the Random Forest (RF) model exhibited the best predictive performance, while BiLSTM demonstrated strong capabilities in processing temporal data. Additionally, the study reveals that writing tasks, due to their structured nature, are easier to assess, whereas speaking tasks present more challenges due to individual variability. The findings suggest that the combination of biotechnology and AI can significantly support the design of personalized learning paths in English education, promoting the interactive and intelligent transformation of educational models, and facilitating the efficient commercialization of biotechnology innovations.