Data-driven approaches to mitigate academic stress and improve student mental health
1 Faculty of Health and Life Sciences, De Montfort University, Leicester, United Kingdom.
2 Education Studies, School of Education, University of Hull, Hull, United Kingdom.
3 Department of Information Technology School of Leadership, Information Technology, University of the Potomac, DC, Washington DC, USA.
4 College of Medicine, Faculty of Nursing, Department of Nursing, University of Ibadan, Ibadan, Nigeria.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 2201-2206
Publication history:
Received on 12 November 2024; revised on 21 December 2024; accepted on 23 December 2024
Abstract:
This paper critically examines the efficacy and transformative potential of data-driven methodologies to assess, monitor, and mitigate academic stress, thereby enhancing student mental health. We aim to uncover latent stress patterns and trigger points within academic environments by utilizing a robust framework of advanced analytics, machine learning, and predictive modeling. Applying these technologies allows for the strategic customization of interventions tailored to individual and group needs in real time. By synthesizing data across multiple educational settings—including K-12 schools and higher education institutions—this study provides comprehensive insights into how varied data sources and modeling techniques can be harmonized to effectively detect and address student stress. The outcomes highlighted in this paper demonstrate the significant impact of data-driven methodologies not only in improving student well-being but also in fostering an educational atmosphere that prioritizes mental health. Our findings underscore the critical role that technological integration in educational strategies plays in revolutionizing student support systems and setting a new standard for mental health care within academic institutions.
Keywords:
Predictive Analytics; Machine Learning; Academic Stress; Student Mental Health; Educational Interventions
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0