A Study on the Impact of Decision Tree and Multiple Regression on Study Time Optimization and Performance

Tianyu Fu

2025

Abstract

Student achievement is affected by a variety of factors, such as study time, prior grades, extracurricular activities, hours of sleep, and a number of practice problems. In this paper, multiple linear regression model and decision tree models are used for modeling and analysis. The results of multiple linear regression showed that prior grades, study time, and the number of practice problems were significant factors affecting students' performance, with students' study time having a highly significant positive effect on students' academic performance. In addition, the decision tree model further indicates the different trends of students' performance under changes in study time for different prior achievement intervals, which provides data support for the development of personalized learning strategies. Based on the findings, this paper provides recommendations for optimizing students' time management, enhancing after-school exercise practice, and developing individualized learning plans for students at different achievement levels. Future research could introduce additional variables, such as mental health and family support, to improve the predictive power of the model and inform education policy optimization.

Paper Citation

in Harvard Style

Fu T. (2025). A Study on the Impact of Decision Tree and Multiple Regression on Study Time Optimization and Performance. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 624-631. DOI: 10.5220/0013703000004670

in Bibtex Style

@conference{icdse25,
author={Tianyu Fu},
title={A Study on the Impact of Decision Tree and Multiple Regression on Study Time Optimization and Performance},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={624-631},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013703000004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - A Study on the Impact of Decision Tree and Multiple Regression on Study Time Optimization and Performance
SN - 978-989-758-765-8
AU - Fu T.
PY - 2025
SP - 624
EP - 631
DO - 10.5220/0013703000004670
PB - SciTePress