A Systematic Study of Student Stress Prediction in Education Sector Using Machine Learning

A Systematic Study of Student Stress Prediction in Education Sector Using Machine Learning

[1]Manoj Kumar V,  [2]Mayur V, [3]Bharath M, [4]Khajabandur N M

Information and Science and Engineering, Malnad College of Engineering

Hassan-573202, India

Email id:manojkumarv724@gmail.com, abj@mcehassan.ac.in, mayurkainij@gmail.com, mbharath0326@gmail.com mittalakodkhajabandu@gmail.com

Abstract—The mental well-being of students is increasingly important in educational institutions around the world. If the academic stress is not managed properly it will lead to various psychological problems affecting the overall performance of students. This paper surveys the use of ML techniques to predict and analyze student stress[1][2]. Various studies using ML techniques such as SVM, regression, random forests, decision trees, and artificial neural networks (ANNs) were reviewed to investigate their effectiveness in predicting students' stress levels based on different datasets and factors[11]. This review highlights the strengths and limitations of existing studies, identifies gaps in the literature, and makes gateway for further research to improve real-time stress prediction systems for students.

Keywords— Student Stress, SVM, Regression, Decision Tree, Machine Learning, Education, Data Science, Academic, Results.