Predicting the Impact of Sleep Patterns on Student Academic Performance Using Machine Learning

Gayathri V P, Harisowndharya V, Haritha Saraswathy R, Gokul R

2025

Abstract

Adequate sleep has a significant role in improving cognitive skills, especially memory retention. Sleep deprivation at night and the consequent daytime sleepiness affect the physical and mental health of students and their academic performance. The objective of this study is to build a predictive model using machine learning and investigate the sleep pattern and its association with college students’ academic performance. It is a cross-sectional study conducted among 375 undergraduate college students across various disciplines. A questionnaire that contained questions on demography, sleep habits, academic performance, and ideal sleep was used to collect data. The supervised classification algorithms such as Decision Tree, Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest are used to classify and predict the effect of sleeping behaviours on college students’ academic performance. It is determined that the Decision Tree prediction model has an accuracy of 97.33% in the classification prediction of academic performance. It is observed that sleep duration is positively correlated with better academic performance. Key predictive factors for academic performance include sleep hours, sleep quality, stress levels, and electronic device usage before bed. These findings provide quantitative, objective evidence that better quality, longer duration, and greater sleep consistency are strongly associated with better academic performance in college. The Decision Tree model proves to be highly effective, emphasizing the relevance of sleep habits in predicting academic outcomes. This implies that educators can use certain sleeping habits to improve student support services and increase the overall effectiveness of the educational system. Our work involving research on sleep patterns and the direction of sustainable development in academic performance training proved to be encouraging.

Paper Citation

in Harvard Style

V P G., V H., R H. and R G. (2025). Predicting the Impact of Sleep Patterns on Student Academic Performance Using Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 862-870. DOI: 10.5220/0013606200004664

in Bibtex Style

@conference{incoft25,
author={Gayathri V P and Harisowndharya V and Haritha R and Gokul R},
title={Predicting the Impact of Sleep Patterns on Student Academic Performance Using Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={862-870},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013606200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Predicting the Impact of Sleep Patterns on Student Academic Performance Using Machine Learning
SN - 978-989-758-763-4
AU - V P G.
AU - V H.
AU - R H.
AU - R G.
PY - 2025
SP - 862
EP - 870
DO - 10.5220/0013606200004664
PB - SciTePress