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Published in: BMC Public Health 1/2024

Open Access 01-12-2024 | Research

Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods

Authors: Zhiyuan Sun, Yunhao Yuan, Xuan Xiong, Shuqiao Meng, Yifan Shi, Aiguo Chen

Published in: BMC Public Health | Issue 1/2024

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Abstract

Background

Elevated levels of executive function and physical fitness play a pivotal role in shaping future quality of life. However, few studies have examined the collaborative influences of physical and mental health on academic achievement. This study aims to investigate the key factors that collaboratively influence primary school students' academic achievement from executive function, physical fitness, and demographic factors. Additionally, ensemble learning methods are employed to predict academic achievement, and their predictive performance is compared with individual learners.

Methods

A cluster sampling method was utilized to select 353 primary school students from Huai'an, China, who underwent assessments for executive function, physical fitness, and academic achievement. The recursive feature elimination cross-validation method was employed to identify key factors that collaboratively influence academic achievement. Ensemble learning models, utilizing eXtreme Gradient Boosting and Random Forest algorithms, were constructed based on Bagging and Boosting methods. Individual learners were developed using Support Vector Machine, Decision Tree, Logistic Regression, and Linear Discriminant Analysis algorithms, followed by the establishment of a Stacking ensemble learning model.

Results

Our findings revealed that sex, body mass index, muscle strength, cardiorespiratory function, inhibition, working memory, and shifting were key factors influencing the academic achievement of primary school students. Moreover, ensemble learning models demonstrated superior predictive performance compared to individual learners in predicting academic achievement among primary school students.

Conclusions

Our results suggest that recognizing sex differences and emphasizing the simultaneous development of cognition and physical well-being can positively impact the academic development of primary school students. Ensemble learning methods warrant further attention, as they enable the establishment of an accurate academic early warning system for primary school students.
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Metadata
Title
Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods
Authors
Zhiyuan Sun
Yunhao Yuan
Xuan Xiong
Shuqiao Meng
Yifan Shi
Aiguo Chen
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-024-17769-7

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