Identification of Social Anxiety in High School: A Machine Learning Approaches to Real-Time Analysis of Student Characteristics

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Abstract

Students in high school commonly struggle with social anxiety, which has a negative effect on both their academic performance and emotional health. The various forms of social anxiety that students at Little Scholars Matriculation Hr. Sec. School in Thanjavur, Tamil Nadu, India, exhibit become the subject of this study. The study uses a strong analytical framework to investigate social phobia experiences by utilizing techniques like machine learning, clustering techniques, data exploration, and correlation analyses. A measurable increase in distress with the severity of social phobia is revealed by visual plots based on answers to a 17-item Social Phobia Inventory (SPIN) questionnaire. Correlation analyses clarify complex relationships between survey items, revealing the complex dynamics of high school social interactions. Using clustering techniques, different subgroups of students are found within the student population according to shared or unique traits related to social anxiety. By utilizing machine learning, the latent features linked to every survey question offer a more thorough comprehension of the factors affecting the reported levels of distress. In addition to defining social anxiety, the study draws attention to particular social phobia characteristics at Little Scholars School. In order to address identified fears, the research suggests an innovative strategy that involves creating customized scenarios using Virtual Reality (VR) and Augmented Reality (AR) technologies. This creative method emphasizes the cooperation of experts in psychology, education, and technology across disciplinary boundaries, providing a focused and immersive approach to reduce social anxiety. The study concludes by making recommendations for future paths for widespread adoption and ongoing investigation of cutting-edge technological advancements in mental health support systems, in addition to highlighting the possible advantages of VR and AR therapy for high school students.

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APA

Abinaya, M., & Vadivu, G. (2024). Identification of Social Anxiety in High School: A Machine Learning Approaches to Real-Time Analysis of Student Characteristics. IEEE Access, 12, 77932–77946. https://doi.org/10.1109/ACCESS.2024.3407885

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