Fostering Resilience: Machine Learning Models for Student Stress Prediction in Education

This paper explores the complex field of student stress analysis. Recognizing that stressors in educational contexts are complicated, this work makes use of cutting-edge Machine and Deep learning approaches to negotiate the complexities of stress factors unique to individual students. The main objective is to improve the stress detection models' capacity for prediction and generalization, thereby tackling the difficulties caused by the dearth of labeled data in educational settings. The study emphasizes how crucial early stress identification is to the prevention of many stress-related health issues. Using many biosignals, including thermal, electrical, impedance, auditory, and optical signals, the research finds patterns that correspond to different stress levels. Stress analysis is based on the dataset, which has about 20 carefully chosen elements based on Psychological, Physiological, Social, Environmental, and Academic Factors. The dataset is classified using a variety of models, such as Deep Learning, Generalized Linear Model, Random Forest, Gradient Boosted Trees, Decision Trees,and Support Vector Machine, to create a pattern for stress detection. Interestingly, the Deep Learning model performs better than other classifiers in stress detection. This study aims to investigate how machine learning can enhance the effectiveness of topic-self-governing stress detection algorithms in a revolutionary manner. The study adds to the current efforts to enhance early diagnosis and intervention options for student stress, hence encouraging higher overall well-being, by illuminating the potential of cutting-edge technologies.