Predicting academic performance associated with physical fitness of primary school students using machine learning methods

The prediction model of academic achievement based on cardiorespiratory fitness and BMI status for ninth-grade students

The purpose of this study was to predict an academic achievement model based on cardiorespiratory fitness (CRF) and body mass index (BMI) in ninth-graders. The study sample included 6 530 adolescents from 341 public schools in Slovakia. Criterion-referenced competency tests measuring academic performance in mathematics and mother language (Slovak), CRF, and BMI were assessed in the academic year 2022-2023. The results from the Random Forest Regression (RFR) machine learning algorithm suggest that adolescents who meet the international CRF and BMI criterion-referenced standards have a higher probability of getting a higher academic achievement score than unfit students with overweight or obesity. The chances of achieving the highest level of academic performance rose by 165% in mathematics and by 484% in mother language for boys who were fit and of normal weight compared to unfit boys with obesity. Unfit boys with obesity and unfit overweight girls had significantly lower odds of having the highest level of academic achievement compared to fit and normal-weight adolescents in mathematics (OR = 0.38; 95% CI, 0.20-0.71; p = 0.003; OR = 0.32; 95% CI, 0.18-0.55; p < 0.001) and mother language, respectively (OR = 0.17; 95% CI, 0.09-0.34; p < 0.001; OR = 0.17; 95% CI, 0.08-0.38; p < 0.001). Our results suggest that CRF is a significant predictor, with fit and normal-weight boys showing higher odds of better academic performance, but the model's modest predictive power suggests other factors also play a role.

Sex-specific associations of muscular fitness with overall academic performance and specific school subjects in adolescents: the EHDLA study

Introduction

Overalll, muscular fitness and academic performance are two variables widely studied in the literature. However, evidence on the relationship between muscular fitness and specific school subjects (e.g., physical education), as well as their differences by sex, is scarce.

Objective

The aim of this study was to examine the associations between muscular fitness and overall academic performance and between muscular fitness and specific subjects (i.e., language, math, foreign language, and physical education) in a sample of secondary Spanish school students.

Methods

For the present study, a sample of 766 students (45% boys, aged 12-17 years). From the Eating Healthy and Daily Life Activities (EHDLA) study was used. The Assessing the Levels of Physical Activity and Fitness (ALPHA-FIT) battery was used to determine handgrip strength and lower body muscular strength. The performance of the different school subjects was provided by the school centers.

Results

Overall, for both boys and girls, higher muscular fitness was associated with greater academic results, with the greatest differences in physical education. However, only significant differences were identified for girls.

Discussion and conclusion

Global muscular fitness is important for contributing to cognitive development, both in physical education and in the rest of the subjects of the educational curriculum for adolescents. At the same time, although the association appears to follow the same trend for both sexes, the results seem more evident for girls.

Mental fatigue mediates the relationship between qi deficiency and academic performance among fifth-grade students aged 10-13 years

Background

Health has effects on children's academic performance. Qi deficiency is generally used to assess an individual's health in the Chinese traditional medicine theory. This study explores the effects of qi deficiency on children's academic performance and examines whether mental fatigue mediates these effects.

Methods

A total of 550 students aged 10-13 in fifth-grade were surveyed in a big primary school in Sichuan Province in November 2023 using paper-pencil-based questionnaires. Qi deficiency and mental fatigue were assessed, and exam scores in Chinese and Mathematics were recorded. Pearson's correlation and linear regression analyses were used to test the mediation model and hypotheses.

Results

The fifth-grade students had mild qi deficiency (M = 2.09) and a mild state of mental fatigue (M = 2.38) on a five-point Likert scale. The average exam scores in Mathematics and Chinese were 70.07 and 74.44 points out of 100, respectively. Qi deficiency was associated with Mathematics scores (r = -0.37, p < 0.01) and Chinese scores (r = -0.30, p < 0.01), and mental fatigue (r = 0.47, p < 0.01). Furthermore, mental fatigue was associated with Mathematics scores (r = -0.46, p < 0.01) and Chinese scores (r = -0.34, p < 0.01). Linear regression analyses showed that qi deficiency significantly predicted Mathematics scores (β = -0.26, p < 0.01), Chinese scores (β = -0.19, p < 0.01), and mental fatigue (β = 0.41, p < 0.01). When qi deficiency was controlled for, mental fatigue significantly predicted Mathematics scores (β = -0.28, p < 0.01) and Chinese scores (β = -0.17, p < 0.01).

Conclusion

The mediation model and hypotheses were well supported, indicating that mental fatigue mediated the influence of qi deficiency on academic performance of fifth-grade students. Furthermore, the mediation effect of mental fatigue on Mathematics scores was a little stronger than that on Chinese scores.

Differences in Physical Activity, Sedentary Behavior, Health-Related Physical Performance Indices and Academic Achievement: A Comparative Study of Normal-Weight and Obese Children in Qatar

Background: The relationship between physical activity (PA), health-related physical performance (PP), and academic achievement (AA) plays an important role in childhood. This study examined the differences in PA, sedentary behavior, health-related PP, maturity status, and AA between normal-weight and obese school children in Qatar. Methods: Eighty schoolchildren were recruited (age: 12.1 ± 0.6 years). Based on age-specific BMI percentiles, the children were classified as normal weight (n = 40) or obese (n = 40). Moore's equations were used to estimate their maturity status (PHV). The measurements encompassed anthropometric data as well as PP tests (medicine ball throw, postural stability, handgrip strength). AA was assessed by reviewing school records for grade point average in Mathematics, Science, and Arabic courses. The total amount of time spent participating in PA each week was calculated using the International Physical Activity Questionnaire Short Form. Results: Handgrip strength was the only parameter that showed a relevant group difference (p < 0.001, ηp2 = 0.15; normal weight: 19.7 ± 3.46 N; obese: 21.7 ± 2.80 N). We found only one moderate correlation between PHV and handgrip strength (r = 0.59). Conclusions: The findings suggest that obesity status alone might not serve as a sufficient predictor of AA in school or PA levels.

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

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.

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.