International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Multifaceted Machine Learning-Based Approach for Holistic Student Well Being and Academic Success in Sri Lankan Schools

Ariyarathna H.M.M.MB.Sc (Hons) in Information Technology, Specializing In Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaNethmi G.W.MB.Sc (Hons) in Information Technology, Specializing In Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaAdikari A.A.D.N.VB.Sc (Hons) in Information Technology, Specializing In Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSamadhi RathnayakeDepartment of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri LankaKapila DissanayakaDepartment of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 8 No 12 (2024): Volume 8, Issue 12, December 2024 | Pages: 1-10

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 09-12-2024

doi Logo doi.org/10.47001/IRJIET/2024.812001

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Abstract

This research project aims to advance educational outcomes by integrating three key components: predictive modeling for academic performance, social network analysis, and early detection of mental health issues among students including adverse mental health, among learners. The machine learning approaches of the project will create statistical models to predict performance of students and evaluating them for poor performers. Using graph analytic techniques, it will also investigate influence and coordination in the student social networks to understand the effects of social interactions on attitude and behavior toward academics. Furthermore, the study will employ sophisticated multiple telemetry sentiment and emotion analysis tools to identify symptoms of emerging mental health illnesses among the students studying in grade 6 to 9. The goal of this multifaceted method which involves data acquisition from the academic records, social communication level, and effective expressions is to empower educators along with the mental health professionals with effective tools for intervention. The goal is to have an effective learning environment that corresponds to students’ needs and expectations and in which they can improve their academic achievements as well as their psychological well-being.

Keywords

predictive models, academic achievement, social networks, peer pressure, collaboration patterns, mental health status, multimodel data analysis and machine learning, student welfare, learning effectiveness


Citation of this Article

Ariyarathna H.M.M.M, Nethmi G.W.M, Adikari A.A.D.N.V, Samadhi Rathnayake, & Kapila Dissanayaka. (2024). A Multifaceted Machine Learning-Based Approach for Holistic Student Well Being and Academic Success in Sri Lankan Schools. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(12), 1-10. Article DOI https://doi.org/10.47001/IRJIET/2024.812001

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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