International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Teacher–Student Inter-Brain and Behavioral Synchronization Analysis in Remote Education Using Hybrid CNN–LSTM and Explainable AI |
|---|---|
| ABSTRACT | The widespread adoption of remote education has transformed the modern learning environment by enabling flexible and accessible knowledge delivery. Despite its advantages, online learning platforms often face challenges related to student engagement, interaction quality, and effective communication between teachers and learners. The absence of direct classroom interaction makes it difficult for educators to assess student attention, participation, and learning behavior in real time. Consequently, understanding the synchronization between teachers and students has become an important research area in educational technology. This study proposes an intelligent framework for analyzing teacher–student inter-brain and behavioral synchronization in remote learning environments using a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture integrated with Explainable Artificial Intelligence (XAI). The proposed framework evaluates educational interaction data, including participation rate, response time, engagement score, attendance consistency, and assessment performance, to identify synchronization patterns between teachers and students. CNN is utilized for extracting behavioral features, while RNN captures temporal learning patterns and sequential dependencies. The integration of Explainable AI enhances transparency by providing interpretable recommendations based on synchronization predictions. The proposed system classifies synchronization levels into Low, Medium, and High categories and generates actionable recommendations that support adaptive teaching strategies. Experimental analysis demonstrates that the hybrid CNN–RNN framework achieves high prediction accuracy and effectively identifies engagement-related patterns in remote learning environments. The findings highlight the potential of intelligent synchronization analysis for improving educational effectiveness, enhancing learner engagement, and supporting data-driven decision-making in digital education systems. |
| AUTHOR | G. REVATHI, R.HARIPRIYA Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407061 |
| pdf/61_Teacher–Student Inter-Brain and Behavioral Synchronization Analysis in Remote Education Using Hybrid CNN–LSTM and Explainable AI.pdf | |
| KEYWORDS | |
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