Affiliations: [a] Faculty of Physics, VNU University of Science, Hanoi, Vietnam | [b] Vietnam National University, Hanoi, Vietnam | [c] Ivycation Company, Hanoi, Vietnam | [d] Reigate Grammar School of Vietnam, Hanoi, Vietnam | [e] Hanoi-Amsterdam High School for the Gifted, Hanoi, Vietnam | [f] Nguyen Sieu School, Hanoi, Vietnam
Correspondence:
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Corresponding authors. Le Quang Thao. E-mail: [email protected] and Nguyen Thi Bich Diep, E-mail: [email protected].
Abstract: Vietnamese students are facing significant academic pressure due to societal and familial expectations, which leads to an unfavorable learning environment. We aim to employ a temporary spatial-temporal stress monitoring system. Using Wireless Sensor Network (WSN) technology, it collects data on students’ emotional states and incorporates a prediction model, “Reduce Students’ Stress in School” (R3 S), to detect students’ emotional states across school premises. The integration of R3 S and WSN is conducted in three stages. Initially, sensor nodes are deployed in schools to collect emotional data. Subsequently, we introduce a novel hybrid model combining a one-dimensional Convolutional Neural Network with Long Short-Term Memory networks (1D-CNN-LSTM) to generate a predictive emotional map. This model’s performance, evaluated using RMSE and MAE metrics, shows exceptional precision compared to other LSTM models. When predicting the “stress” condition, the R3 S model achieved a Mean Absolute Error (MAE) of 10.30 and a Root Mean Square Error (RMSE) of 0.041. Lastly, we generate a comprehensive map of cumulative emotional conditions, serving as a guide for school counselors. This map aids in fostering a healthy, conducive learning environment.
