Evaluation of School Students Performance Using Machine Learning

N. T. Renukadevi, K. Saraswathi, E. Roshini, M. G. Lakshitha, S. Pratheeksha

2025

Abstract

In today's educational perspective, the need for data-driven insights to enhance student outcomes is increasingly recognized. In this paper we are going to develop a machine learning model to predict and evaluate student performance based on various academic and demographic factors. This system will utilize past information about students like their grades and background to provide educators better suggestions on how to assist students in choosing their academic group for the 11th grade. The dataset will be prepared by collecting information from school students through Google forms. To predict and evaluate student’s performance, we apply four distinct machine learning models like Decision Trees, Random Forest, Support Vector Machines (SVM), and Logistic Regression. This research exposes the application of machine learning in guiding the selection of academic groups with promising results and significant potential in educational settings. Furthermore, the research underscores the importance of using data-driven approaches to support educators in making informed decisions and also this can assist in altering personalized interventions enhancing learning outcomes for all students.

Paper Citation

in Harvard Style

Renukadevi N., Saraswathi K., Roshini E., Lakshitha M. and Pratheeksha S. (2025). Evaluation of School Students Performance Using Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 503-512. DOI: 10.5220/0013622900004664

in Bibtex Style

@conference{incoft25,
author={N. T. Renukadevi and K. Saraswathi and E. Roshini and M. G. Lakshitha and S. Pratheeksha},
title={Evaluation of School Students Performance Using Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={503-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013622900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Evaluation of School Students Performance Using Machine Learning
SN - 978-989-758-763-4
AU - Renukadevi N.
AU - Saraswathi K.
AU - Roshini E.
AU - Lakshitha M.
AU - Pratheeksha S.
PY - 2025
SP - 503
EP - 512
DO - 10.5220/0013622900004664
PB - SciTePress