Dynamic identification model of psychological state in Ideological and Political Education based on biosensing
Abstract
Individuals’ psychological states greatly influence how they participate and react to educational processes, especially when it comes to ideological and political education. Ideological and political education (IPE) is an essential component of educational systems that aims to develop a sense of national identity, social duty, and confidence in students. This research explores the application of biosensor technologies to analyze students’ psychological states within the context of ideological and political education. Students’ lifestyles and stress levels often lead to psychological issues, but conventional IPE techniques lack real-time, individualized data for effective mental health treatment. This research introduces a model, Efficient Osprey Optimized Dynamic Long Short-Term Memory (EOO-DLSTM), to identify the psychological state for Ideological and Political Education utilizing biosensing technologies to assess students’ stress and emotional states in real-time. The model uses biosensors to collect real-time physiological data that reflects the psychological state of students. The data was preprocessed using a Gaussian filter to remove noise from biosensor data. Power spectral density (PSD) is used to extract the features from preprocessed data. EOO is used to optimize and select the feature from the biosensor, and DLSTM can be employed to identify the psychological state. Based on experimental findings, the model can accurately identify the psychological states of students, including information about their stress levels and emotional involvement. The proposed EOO-DLSTM outperforms the existing systems such as Accuracy (95.32%), Precision (93.97%), Recall (96.18%), and F1 score (97.62%). The EOO-DLSTM model surpasses traditional models through the utilization of advanced optimization techniques for enhanced accuracy in recognizing psychological states from biosensor data. It is effective in handling overlapping features and complex temporal dependencies, thus being very suitable for real-time monitoring of mental health. The approach emphasizes how biosensing technologies can be used in educational frameworks to support students’ overall development.
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