Construction of evaluation model of university ideological and political education effect based on biosensor technology
Abstract
The objective of ideological and political education (IAPE) in higher education has gained much concentration to facilitate students increasing their moral principles, sense of community responsibility, and cultural responsiveness. A methodical evaluation approach for determining the efficiency of IAPE in institutions prepared with biosensor technology is obtained in this work. This study proposes to impartially assess how ideological education affects students’ mental and emotional states in recognition of the important role that psychological health education plays in promoting students’ mental health. A detailed student evaluation model was produced based on biosensor data, facial emotion recognition, and EEG. The model leverages an Enhanced Sailfish Optimized Flexible Deep Belief Networks (ESO-FDBN) to recognize and track facial expressions, as EEG signals capture fundamental cognitive responses throughout educational sessions. Following normalization, trends in contribution and understanding of ideological content are identified by analyzing these data. The results demonstrate that the recommended approach significantly increases classification accuracy over conventional techniques by utilizing statistical features from EEG data and emotion tracking. The proposed ESO-FDBN model established its stable and well-balanced performance with 98.5% accuracy, 97.2% precision, 96.8% recall, and a 97.0% F1 score. The results show that students’ ideological configuration and participation are significantly influenced by demographic characteristics, campus culture, and social practices. This study shows that biosensor technology can be used to evaluate how efficient civic and ethical education is and concrete the way to more advanced teaching methods that take into account the cognitive and emotional demands of participants.
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