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Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods.
Sun, Zhiyuan; Yuan, Yunhao; Xiong, Xuan; Meng, Shuqiao; Shi, Yifan; Chen, Aiguo.
Afiliação
  • Sun Z; College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
  • Yuan Y; Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou, 225127, China.
  • Xiong X; School of Information Engineering, Yangzhou University, Yangzhou, 225127, China.
  • Meng S; Department of Physical Education, Nanjing University, Nanjing, 210033, China.
  • Shi Y; Department of Physical Education, Xidian University, Xian, 710126, China.
  • Chen A; College of Physical Education, Yangzhou University, Yangzhou, 225127, China.
BMC Public Health ; 24(1): 274, 2024 01 23.
Article em En | MEDLINE | ID: mdl-38263081
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sucesso Acadêmico Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male País/Região como assunto: Asia Idioma: En Revista: BMC Public Health Assunto da revista: SAUDE PUBLICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Sucesso Acadêmico Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male País/Região como assunto: Asia Idioma: En Revista: BMC Public Health Assunto da revista: SAUDE PUBLICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China