Real-Time Feedback and Tutoring of College English Using Artificial Intelligence Algorithms and IoT Sensors

In smart education, providing real-time feedback and personalized tutoring is crucial for enhancing student learning outcomes. Traditional methods of college English tutoring fail to capture students’ immediate learning needs and progress. To address these challenges, a novel approach called visual, auditory, reading/writing, and kinesthetic (VARK) is proposed for the automatic prediction of students’ learning styles. Data gathered from the Internet of Things (IoT) are used to monitor students’ behaviors and interactions. Learning behavioral data is categorized into VARK categories using the weighted K-means clustering technique. Using a clustering technique, behavioral patterns are systematically analyzed and matched to a specific learning style. Further, an advanced deep learning (DL) model called neuro-fuzzy adaptable long short-term memory (NeuroFuz-ALSTM) is utilized to examine and categorize students’ learning sequences into distinct styles. In addition, bidirectional encoder representations from transformers (BERT) for feedback analysis are applied to enhance the learning experience. Experimental results show that the system significantly enhances the learning experience, improving student engagement, retention, and overall English language proficiency. This approach represents a major step toward creating a more responsive and efficient real-time feedback system in educational environments.