Research on interactive english classroom teaching based on biosensor technology: Analysis of biological indicators
Abstract
With the advancement of educational technology, biosensors are becoming valuable in enhancing classroom interactivity and adapting teaching strategies. In English language classrooms, maintaining student engagement and managing learning anxiety is essential for effective learning; traditional methods fail to offer real-time insights into student engagement and emotional states. The objective of the research was to enhance language instruction effectiveness by monitoring learners’ cognitive states using biosensor technology. Initially, biosensors were used to collect physiological data such as heart rate variability, eye movement, facial expression, posture, and seating data from students during English language lessons and also gathered over four weeks in a controlled classroom setting. The collected data underwent noise reduction using signal-to-noise ratio (SNR) to improve signal clarity and min-max normalization to scale the data within a consistent range for accurate analysis. Spiking neural networks (SNNs) are integrated with biosensors and areic brain neural processing, enabling dynamic adaptation of teaching content based on physiological signals and enhancing personalized learning by responding to student’s cognitive and emotional states. The findings offer that biosensor technology combined with SNNs significantly improves student engagement, reduces language anxiety, and increases learning efficiency. When compared to other weeks, student engagement (30%), cognitive load (10%), task completion efficiency (30%), attention focus (35%), and teacher-student interaction (35%), all showed better outcomes in Week 4. This suggests that biosensor-driven adaptive teaching, powered by SNNs, has the potential to transform interactive language learning.
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