The Role of Emotional Biosensing Feedback in Evaluating the Effectiveness of Ideological and Political Education Teaching

Ideological and Political Education (IPE) makes a significant impact on how they can develop their values, beliefs, and perceptions of emotions. To amplify students’ feeling connected to what they are learning, biosensors such as Heart Rate Variability (HRV), GSR, and EEG are being applied to optimize the way students react emotionally during various activities. In this case, the objective of the research is to deliver biosensing emotional feedback to inform the effectiveness of transmitting political education and ideology. It examines the pedagogy of employing physiological sensors, particularly EEGs, to monitor students’ cerebral and emotive responses to IPE lectures. Based on the arguments put forward here, the research puts forward a new Stochastic Fractal Search-driven Adaptive Deep Belief Networks (SFS-ADBNs) model for predicting and assessing the students’ emotional and attention profiles. Clean data are paramount, and as such, the data were preprocessed by following the following steps, which included median filtering and artifact removal. About the preprocessed data, PSD is used to extract the features. I can use methods such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to quantify a learner’s attentiveness or inattentiveness. The recall achieved was as high as 91.4%, with averaged F1 score of 89.68%, accuracy of 93.45%, and precision of 90.3%. Experiments reveal that the advocated technique yields better than traditional techniques. It highlights how feedback from biosensing emotional data, evaluating the emotionally engaged participation and the cognitive attention, effectively augments the delivery of IPE. This allows an educator to alter the course plans and content based on present-day emotions and attending data.