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Abstract

Previous studies have indicated a positive association between physical activity (PA) and mathematics achievement. The present study explored the association of PA outside school with math interest using longitudinal data from Kaifeng, a medium-sized city in Central China. A two-tier multilevel logistic model was conducted using 2,132 students’ math test scores, self-reported PA frequency, and math learning-related experience in December 2019 and January 2021. Socioeconomic status, school location, and sex were all controlled. The results showed that physical exercise frequency and physical exercise per class outside school had a significant positive impact on mathematics interest ( $β = 0.072, p = 0.001$ ; $β = 0.153, p = 0.043$ ), as did previous mathematics interest ( $β = 0.581, p = 0.000$ ) and self-educational expectation ( $β = - 0.232, p = 0.033$ ; $β = - 0.432, p = 0.000$ ). It indicated that regular physical exercise outside of school can lead to increased mathematics interest.

Keywords

multilevel logistic model longitudinal analysis middle school students in China

Introduction

It is well-established that physical activity (PA) benefits students’ health, cognitive function, and educational outcomes (Watson et al., 2017). Executive function refers to the neurocognitive processes that maintain an appropriate problem-solving set to attain a later goal improved by PA (Chang et al., 2012). It seems essential to explain the PA-related promotion of academic achievement promotion for its significant effect on academic achievement, school readiness, and school success (Diamond, 2013). Mathematics academics are favored in this kind of analysis because of their great importance in the curriculum, its useful structure as a measure, and the tight association with executive function.

Though there is some support from mediator analysis, results have been inconsistent. Studies have reported that not all PA interventions led to improved mathematical test scores, and cognitive test scores did not consistently change with mathematical test scores (Donnelly et al., 2016; Fedewa et al., 2015). Meta-analysis and narrative review have attributed these inconsistent results to inefficiency in interventional dosage and duration, unsatisfactory implementation, off-targeted measure selection, unstandardized outcome measures, and inequality between tests and scales (X. Ma & Tang, 2021; Singh et al., 2018; Watson et al., 2017). They have proposed better experimental design, appropriate measures, and strict monitoring and implementation to improve consistency. However, there is a risk of reversing the causality because cognitive development was not the only predictor of students’ academic achievement, and academic achievement provided little information about how PA worked in the learning process. Thus, further study is needed to explore the associations between PA and learning process-related influential factors instead of focusing on the quantitative characteristics of PA, such as amount, frequency, and duration.

Learning interest is essential to the learning process. It is positively associated with students’ cognitive development (Rotgans & Schmidt, 2017; D. Zhang et al., 2021), learning activities, and academic achievements. Dewey (1913) emphasized that intrinsic motivation contributed more than effort. Mathematics interest, for example, is a promising factor driving students to engage and enjoy learning mathematics (Koller et al., 2001; Reeve et al., 2015; Schiefele & Csikszentmihalyi, 1995; Simpkins et al., 2006), establishing better learning involvement in class (Cai & Leikin, 2020), and leading to positive outcomes (Cai & Leikin, 2020; Reeve et al., 2015; Schiefele, 1996; D. Zhang & Wang, 2020). It also encourages students to be more persistent when facing challenges in the learning process (Barnes, 2019).

Though results from primary school students have challenged the connection (Koller et al., 2001), most results from junior high school students have supported the view that mathematics interest significantly impacts students’ mathematics achievement (Jiao, 2008; D. Zhang & Wang, 2020). Therefore, as a promising factor for students’ mathematics achievement, most authoritative academic communities, such as the Organisation for Economic Co-operation and Development (OECD, 2013a, 2013b), the International Commission on Education, and the Mathematics Curriculum Standards for Compulsory Education (2011), emphasize the importance of students’ mathematics interest and advocate for enhancing students’ interest during daily teaching and learning in the classroom (Baumert et al., 1998; Hidi, 1990; Hidi et al., 2002; Rotgans & Schmidt, 2017; D. Zhang & Wang, 2020).

A previous study using a national sample of grade 8 students from China showed an association between PA and mathematics interest. Since students’ daily PA occurs primarily in the school environment, physical education (PE) curriculum implementation, including PE lesson frequency, recess exercise, and extracurricular physical activities in school, was used to estimate students’ weekly amount of PA. The findings showed an indirect effect between PE curriculum implementation and mathematics achievement (Wang et al., 2019). In this multiple mediators model, PE curriculum implementation affected cardiorespiratory fitness, PE interest, and healthy lifestyle. Cardiorespiratory fitness played a mediating role in the relationship between PE curriculum implementation and mathematics achievement with PE interest and healthy lifestyle affecting mathematics interest and mathematics self-confidence and, consequently, students’ academic achievement.

Looking closely at the items in the questionnaire, these results propose an academic correlation between PA and mathematics achievement and support previous evidence that students’ fitness and living habits are two factors influencing mathematics achievement (Correa-Burrows et al., 2017; Hansen et al., 2014). However, there is little additional research strongly indicating an association between PA and mathematics interest. In addition, this sectional model failed to include sufficient variables to understand the associations between PA and mathematical learning behaviors, such as students’ learning efforts, difficulty in learning, self-educational expectation, and tracking data of mathematics interest and previous mathematics achievement. Thus, whether PA can significantly affect students’ mathematics interest when analyzing with mathematics learning variables requires further exploration.

Responding to this need, the present study used nested large sample analysis to explore the association between PA in leisure time and mathematics interest. We hypothesized that junior high school students’ PA in leisure time would predict students’ mathematics interest along with their previous learning interest, previous mathematics interest (the results of the first round of the survey), and self-educational expectations. In the present study, we used PA in leisure referring to the exercise or sports that students participated in off-campus instead of total PA because students’ PA participation in leisure time is generally performed of their own volition, and they perform more actively when engaged. The results would help in understanding the underlying correlations between self-determined PA and mathematics interest.

Materials and Methods

Sample

We mainly analyzed data from Wave 2 of a longitudinal survey project that focused on outside-school learning in a mid-sized city with a moderate level of economic and educational development in central China. The middle schools in the city were divided into three levels by the local education bureau according to educational quality and reputation. Five representative schools of different quality levels were then recommended. Detail of the sample could be seen in our previous studies, such as Y. Zhang et al. (2022) or Ren et al. (2023). The data were collected through a questionnaire survey and administrative data collection. The latter included school-reported final examination scores for the semester and concrete testing items. The questionnaire was completed and collected under the supervision of each class’s headteacher and members of the research team. Wave 1 was administered a week before the final exam of the first semester of grade 8 (students are around age 13 or 14); in December 2019, 2,645 valid responses were received. In January 2021, Wave 2 was administered a week before the final exam of the first semester of grade 9 (students are around age 14 or 15); students from Wave 1 returned 2,599 valid responses. It should be noted that all the students in the city took the same exam at the end of the semester, which was developed by the school district coach.

We used data from the Wave 2 questionnaires, which provided retrospective information on students’ mathematics interest in the first semester of grade 9, and the previous mathematics achievement and previous mathematics interest (the first semester of grade 8) to longitudinally evaluate the effectiveness of mathematics interest during the first semester of grade 9.

Ethics Permission

Approval from the Institutional Review Board (IRB) for the current study from the university of the first author was obtained. Approvals from the school district and school principal were obtained and so did the assent and parental consent forms.

Dependent Variable

The interest of learning in mathematics, which can be seen from its literal meaning, belongs to interest and is an important factor for students to actively participate in mathematics learning (Koller et al., 2001; Reeve et al., 2015; Schiefele & Csikszentmihalyi, 1995; Simpkins et al., 2006) . Taking the students’ mathematics interest in the first semester of grade 9 as the dependent variable, we first set four questions from PISA2012 (e.g., “I like reading mathematics books,”“I’m looking forward to math class,”“I do math because I like it,” and “I’m very interested in what I learned in mathematics”) with scores to sum and calculate their average value (OECD, 2013a, 2013b). We then assigned 0 to those less than or equal to 3 and 1 to the rest and classified them.

Independent Variables

At the individual level, the factors related to PA in leisure time included physical exercise frequency, physical exercise PA duration, and physical exercise per class outside school. The physical exercise frequency is interpreted as the number of times the school arranges physical education classes per week. Physical exercise PA duration is explained as the time of participating in sports exercise. Physical exercise per class outside school is explained by how many times per week you take part in off-campus sports interest classes. Most of the PA-related items employed from the national education assessment titled China National Assessment of Education Quality-Physical Education & Health (CNAEQ-PEH) (Wu et al., 2019). The factors related to mathematics included previous mathematics interest, previous mathematics achievement, private tutoring in grade 9, difficulty in mathematics, self-educational expectations, average listening time per class, and individual homework time (National Survey Research Center at Renmin University of China, 2014). At the class level, we chose key class, class management, class atmosphere, and mathematics teachers’ classroom expansion knowledge as the factors affecting mathematics interest (OECD, 2013a, 2013b). See Table 1 for detailed definitions of independent variables.

Table 1.

Definitions and Measures for all Variables.

Variable	Definitions and measures
Independent variable
Physical exercise frequency	In addition to physical education and extracurricular sports activities organized by the school, how many times do you usually exercise every week? 8 categories: 0 times = 1, once = 2, 2 times = 3, 3 times = 4, 4 times = 5, 5 times = 6, 6 times = 7, 7 times or more = 8
Physical exercise PA duration	How long do you exercise each time? 4 categories: no exercise = 1, within 30 min = 2, 30 min to 1 hr = 3, more than 1 hr = 4
Physical exercise class outside school per week	How many times per week do you take part in off-campus sports interest classes? 4 categories: 0 times = 1, once = 2, 2 times = 3, 3 times or more = 4
Previous mathematics interest	Mathematics interest in the first school semester of grade 8. e.g., “I like reading mathematics books,”“I’m looking forward to math class,”“I do math because I like it,”“I’m very interested in what I learned in mathematics.” z-score, total full score: 20
Previous mathematics achievement	Mathematics achievement scores received at the end of the first school semester of grade 8 (z-score, total full score: 120)
Private tutoring in grade 9	Do you take part in math tutoring in Grade 9? no = 0, yes = 1
Difficulty in mathematics	Are you having trouble learning math now? 5 categories: not difficult at all = 1, not very difficult = 2, generally difficult = 3, a little difficult = 4, Particularly difficult = 5.
Self-educational expectation	Four categories: Junior high school or less (reference group) = 1; Junior college, undergraduate = 2; masters = 3; doctorate = 4
Average listening time per class	Five categories: 40 min = 1 ; 31–40 min = 2; 21–30 min = 3 ; 11–20 min = 4; less than 10 min = 5
Individual homework time	Average daily individual relative homework time from Monday to Friday. less than class homework time = −1,equal to class homework time (reference group) = 0, longer than class homework time = 1
Class level
Key class	If the class was a key class: yes = 1; no = 2
Class management	How does your homeroom teacher manage students? 5 categories: very loose = 1; relatively loose = 2; average = 3; relatively strict = 4; very strict = 5
Class atmosphere	Class learning atmosphere: least protective = 1, less protective = 2, average = 3, more protective = 4, most protective = 5
Expand knowledge	Your math teacher often expands beyond books. 5 categories: completely disagree = 1; disagree = 2; average = 3; Agree = 4; completely agree = 5

Covariates

Dependent variables were affected by individual, family, and class factors. At the individual level, these factors were gender, being an only child, breakfast, and sleep time. Family factors included family monthly income, the number of computers and books at home, parents’ work level, parents’ education level, and educational expectations for their children. Class factors included teachers’ teaching methods, teacher–student relationships, teachers’ support for students, class learning atmosphere, and class competition, which were found to significantly affect mathematics interest among middle school students (N. Zhang, 2012).

Methodology

This study aimed to explore the association of PA, specifically PA in leisure time, with mathematics interest. Two rounds of longitudinal data were collected through questionnaires in December 2019 and January 2021, and administrative data were obtained with the help of mathematics teaching and research staff in the city. After excluding samples with contradictory responses, 2,123 valid samples were obtained, the response rate was 80.26%, including 52 different classes. Expectation maximization, a numerical algorithm that can be used to maximize the likelihood under a wide variety of missing-data models, was used to interpolate the missing-value data (Dempster et al., 1977).

Since the dependent variable was dichotomous, and to achieve deeper data analysis, we used a multilevel logistic model to explore the factors influencing mathematics interest, though the results were similar to those of the single-layer model. We considered the two-tier model of individual and class. This article uses HLM 6.08 to run a multi-layer logical regression model. First, we added the factors of class level into the model, such as key class, class management, class atmosphere whether the math teacher was a headteacher, the age of the math teacher, the number of PE classes per week, and the expanded knowledge of the math teacher in class, to determine their possible impact on students’ mathematics interest (multilevel logistic model 1). Second, we established another model to estimate their collective impact on mathematics interest by determining class-level factors as significant predictors and individual-level predictors (multilevel logistic model 2).

The multilevel logistic models being tested were as follows.

1. The formulated student-level model (multilevel logistic model 1):

Log [π / (1 - π_{0})] = Y_{ij}

Y_{ij} = β_{0 j} + ε_{ij}

The class-level model was expressed as, where represented class j characteristics and was the residual of the class mean.

2. We used the random coefficient model (multilevel logistic model 2) to determine the influence of PE on mathematics interest. This model was built by combining student- and class-level factors identified as significant in model 1. At the student level, the model was formulated as

$P (Y_{ij} = 1) = π_{0}$ , $Log [π / (1 - π_{0})] = Y_{ij}$ , $Y_{ij} = β_{0 j} + β_{1 j} X_{ij} + β_{2 j} Y_{ij} + ε_{ij}$ , At the class level, the model was formulated

as $β_{0 j} = γ_{00} + γ_{0 s} W_{j} + μ_{0 j}$

$Y_{ij}$ is the mathematics interest of the student $i$ from class $j$ , $β_{0 j}$ is the intercept of the regression equation, $β_{1 j}$ is the regression coefficient of the independent variable, $β_{2 j}$ is the regression coefficient of the covariate, $ε_{ij}$ is the unexplained residual of the score of $i$ from class $j$ , $γ_{00}$ is the average grade of all students, $γ_{0 s}$ is the regression coefficient of class-level variables, $W_{j}$ is the variable of class level and $μ_{0 j}$ is the unexplained residual of class level. We further used the random coefficient model to determine the influence of class- and student-level predictors on mathematics interest under fixed slope and intercept.

Results

The descriptive statistics of sports-independent variables are shown in Table 2. In addition to physical education classes and extracurricular sports activities organized by schools, the number of times students exercise varies from zero to more than seven times per week. Half of the students maintain exercise time within 30 min, and 23.8% participate in extracurricular sports interest classes every week.

Table 2.

Description Results of All Variables.

Name of variable	N	%	Name of variable	N	%
Dependent variable			7 times or more	364	17.1
Mathematics interest			Physical exercise PA duration
Assign 0	780	36.7	No exercise	245	11.5
Assign 1	1,343	63.3	Within 30 min	1,080	50.9
Independent variables			30 min to 1 hr	551	26
Physical exercise frequency			More than 1 hr	247	11.6
0 times	312	14.7	Physical exercise per class outside school
Once	233	11	0 times	1,618	76.2
2 times	417	19.6	Once	272	12.8
3 times	385	18.1	2 times	129	6.1
4 times	179	8.4	3 times or more	104	4.9
5 times	151	7.1	Total	2,123	100
6 times	82	3.9

The results of HLM 1 model data show key class, class management, class atmosphere, and mathematics teachers’ expanded knowledge have a significant impact on mathematics interest. Other class-level predictors, such as age, class teacher, and the average number of physical education classes per week, do not appear to be significant. The key classes, class management, class atmosphere, and mathematics teacher extension knowledge are included in the class level of Model 2 to determine whether these variables have an impact on interest.

We use the random coefficient model of Model 2 to determine the impact of class-level predictors and student-level predictors on interest under fixed slopes and intercepts. The results show PA in leisure time, including physical exercise frequency and physical exercise per class outside school, has a significant positive impact on mathematics interest ( $β = 0.072, p = 0.001$ ; $β = 0.153, p = 0.043$ ). The higher the frequency of physical exercises and physical exercise per class outside school, the higher the interest in mathematics learning. Physical exercise PA duration had no significant impact on mathematics interest ( $β = 0.017, p = 815$ ). Among other personal factors, previous interest has a significant positive impact on mathematics interest ( $β = 0.581, p = 0.000$ ). The higher self-expectation, the higher the mathematics interest. On the contrary, tutoring and difficulty in mathematics have a significant negative impact on mathematics interest ( $β = - 0.232, p = 0.033$ ; $β = - 0.432, p = 0.000$ ). Taking private tutoring in the first semester of ninth grade maybe reduce the student’s mathematics interest. The more difficult a student feels in learning mathematics, the lower his interest in mathematics. At the class level, key class and class management have a significant negative impact on mathematics interest ( $β = - 0.325, p = 0.020$ ; $β = - 0.304, p = 0.035$ ), while math teachers’ knowledge expansion has a significant positive impact on mathematics interest ( $β = 0.148, p = 0.036$ ). To further confirm the results of the study, we allow for variations in the intercept, thereby assuming the hierarchical nature of the data. As shown in Table 3, the factors influencing mathematics interest did not change substantially.

Table 3.

Random Coefficient Model Results of Fixed Intercept and Random Intercept.

	Results of the random coefficient model (intercepts fixed)			Results of the random coefficient model (intercepts allowed to vary)
Name of Variables	β	SE	p	β	SE	p
Independent variables
Individual-level
Physical exercise frequency	.072	0.021	.001	.059	0.015	.000
Physical exercise PA duration	.017	0.070	.815	−.061	0.047	.194
Physical exercise per class outside school	.153	0.076	.043	.129	0.052	.016
Previous mathematics interest	.581	0.084	.000	.459	0.065	.000
Previous mathematics achievement	−.090	0.057	.116	−.038	0.05	.472
Private tutoring in grade 9	−.232	0.109	.033	−.147	0.079	.067
Difficulty in mathematics	−.432	0.074	.000	−.347	0.059	.000
Self-educational expectation
Senior high school or lower*
Junior college, undergraduate	.402	0.198	.042	.304	0.153	.052
Master	.971	0.222	.000	.826	0.164	.000
PhD	1.097	0.195	.000	.763	0.152	.000
Average listening time per class	.071	0.064	.265	.049	0.051	.342
Individual homework time
Less than class homework time	−.256	0.103	.038	−.188	0.097	.058
Equal to class homework time*
Longer than class homework time	.059	0.103	.566	.058	0.079	.463
Class level
Key class	−.325	0.141	.020	−.155	0.088	.085
Class management	−.304	0.141	.035	−.183	0.105	.089
Class atmosphere	.095	0.097	.332	.078	0.062	.218
Expand knowledge	.148	0.069	.036	.121	0.048	.015
Covariates
Individual-level
Gender	−.254	0.113	.024	−.181	0.080	.027
Only child	−.056	0.121	.628	.004	0.099	.969
Sleep time	.012	0.049	.804	.010	0.034	.781
Breakfast	.020	0.065	.765	.029	0.046	.527
Family level
Mother’s education
Primary school or less	−.005	0.200	.980	.020	0.160	.900
Junior high school*
Technical secondary school, vocational high school, senior school	.101	0.141	.474	.070	0.113	.537
Junior college, undergraduate	−.115	0.182	.525	−.039	0.137	.776
Master, PhD	−.775	0.378	.040	−.206	0.346	.554
Father’s education
Primary school or less	−.116	0.230	.614	.013	0.173	.939
Junior high school*
Technical secondary school, vocational high school, senior school	−.298	0.145	.040	−.226	0.117	.059
Junior college, undergraduate	−.191	0.172	.267	−.267	0.122	.032
Master, PhD	−.436	0.464	.348	−.597	0.339	.084
Parents’ highest occupation
Lower level*
Middle level	−.018	0.117	.872	.045	0.098	.645
Upper level	.174	0.133	.191	.198	0.101	.056
Parents’ educational expectation
Senior high school or lower*
Junior college, undergraduate	.097	0.210	.644	.041	0.167	.806
Master	−.091	0.231	.693	−.231	0.189	.226
PhD	−.087	0.221	.691	−.160	0.173	.357
Monthly household income	−.004	0.037	.914	.017	0.028	.532
Computers	.015	0.056	.791	.017	0.042	.688
Books	.052	0.043	.233	.045	0.035	.206

Reference group.

Discussion

This study analyzed the effect of students’ PA in leisure time on their mathematics interest in detail to understand whether PA in leisure time benefits students’ mathematics interest over time. Although previous evidence has associated PA with mathematics achievement, research on the effect of PA on mathematics interest is new to the literature.

In line with previous evidence (Fisher et al., 2012), the descriptive statistical results of the present study show a decrease in mathematics interest from grade 8 to grade 9. Similar results also could be observed in elementary school students and in other academic disciplines, such as physics and chemistry. This tendency may be due to the inevitable learning difficulties and excessive burden of studies that increased with grade promotion (Zhang et al., 2021). The findings of the present study partly supported this claim, showing that difficulties in learning mathematics had a negative impact on students’ mathematics interest. Results regarding homework time indicated that spending less and more time than average on homework was associated negatively and positively, respectively, with mathematics interest. However, this contributes little to explaining the causal relationship between study burden and mathematics interest, because spending more time on homework may result from either study burden or self-motivation.

The main contribution of this study is its detailed analysis of the effects of PA on mathematics interest. It extends the existing research conclusions of whether PA impacts students’ non-intellectual education outcomes since mathematics interest is not only an influential factor for the learning process and results but also a crucial educational outcome in itself. While previous studies revealed a positive association between PA and mathematics achievement via data analysis based on the quantitative characteristics of PA and test scores of standardized tests and teacher-designed quizzes (Fedewa et al., 2015), our longitudinal investigation revealed that middle school students’ mathematics interest is associated with their PA in leisure time. This result supplementary indicates that PA’s impact on the learning process is complex and requires further study. Notably, PE in school provides students with the most daily PA opportunities, but not all the students are fully engaged. On the other hand, students put more effort into PA in their leisure time since those activities are mainly self-determined.

PE classes outside school and physical exercise are quite different in regularization, professionalization, and cost. PE classes outside school provide a series of courses on physical skills and knowledge, which run at a fixed time each week and control the duration and amount of PA. In such classes, students engage in PA with appropriate equipment and facilities and make friends with peers in class. The only drawback is the high cost of money and time that not all families are willing to afford. Self-determined physical exercise, on the other hand, is easy to perform but difficult to stick with because of time limitations, study burden, and need for equipment and partners to play with.

As both PE class outside school and physical exercises are significant in predicting students’ mathematics interests, active students are more likely to be interested in mathematics learning. The higher coefficient of PE classes outside school seems to emphasize the importance of PA’s regulation. This may be because students who regularly take part in PA are more likely to be optimistic when facing challenges and difficulties. Skill learning and competitive games teach them how to fight for success and learn from failure, leading to better self-efficacy than in their inactive peers. Moreover, taking part in exercise regularly may result in less anxiety and disappointment, which would help students cope with learning stress and overcome related difficulties.

Notably, the present findings did not contradict studies showing that cognitive profit mediated the correlation between PA and students’ academic performance (Haapala et al., 2014; Hillman et al., 2014; Mullender-Wijnsma et al., 2015; van der Niet et al., 2016). The result of the PA factor may be explained by the executive function acting on the association between learning interest and cognitive functioning and the model of self-control (Baumeister et al., 1998). Executive function, a well-demonstrated benefit of PA, refers to a set of top-down mental processes that contribute to controlling and goal-directed behaviors (Banich, 2009). It is associated with better adaptation of classroom behaviors (Blair & Diamond, 2008; Riggs et al., 2003) including on-task behaviors (Grieco et al., 2009; J. K. Ma et al., 2014) and attention (Best, 2012; Budde et al., 2008; Gallotta et al., 2012, 2015; Schmidt et al., 2016; van Den Berg et al., 2016). Previous studies demonstrated that students’ mathematics interest decreased with age and was influenced by their previous mathematics interest, learning experience, academic expectation, and academic achievement. However, a strong desire for good academic achievement benefits students’ learning interests and more likely leads to success in academic tests (Dowker et al., 2016), which could also be observed in the present results. Thus, executive function and self-regulation share efforts as a resource to maintain students’ mathematics interest.

Analysis of the effect of change on students’ interest revealed that cultivating students active living habits benefits students’ mathematics interest.

Strength and Limitations

The present study extends our knowledge of how PA benefits students’ educational outcomes by taking leisure PA frequency as a predictor of mathematics interest in a multilevel logistic model along with previous mathematics interest, learning efforts, and other relevant factors affecting mathematics achievement. While previous evidence focused on the associations among in-school PA, cognitive improvement, on-task behaviors, and academic achievement, the findings of the present study reveal that PA in leisure time benefits students’ mathematics interest over time. In other words, regular PA would help to awaken and maintain students’ mathematics interest.

Though the present findings did not fully reveal the associations among PA, cognitive development, mathematics interest, and academic achievement, they indicate that PA-related academic achievement may influence not only students’ intelligence but also their non-intellectual learning factors. With the evidence and implications of PA intervention in school, the following research should attach importance to how non-intellectual factors benefit from PA and the associations between PA-related cognitive development and non-intellectual factors. Further, how PA behaviors and PA interests associate with academic interests, efforts, and outcomes, especially students’ on-task behaviors in the classroom. For implementation, teachers and parents should attach importance to reconciling learning via PA for better educational outcomes. According to the advantage of participating in physical exercise and sports over sedentary rest, teachers should encourage students to take full use of PA opportunities in school and consider how to integrate PA into the learning schedule to keep students energetic. Parents, on the other hand, could encourage students to participate in physical exercise or sports regularly off school, keep their company and provide financial and emotional support.

Footnotes

Acknowledgements

We thank all the principals, teachers, and administrative staff who supported the research project as well as the student participants. This research is part of a larger educational study focusing on mathematical private tutoring. Analyzing the association between PA and mathematics interest is unique and distinct from findings reported elsewhere.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Lidong Wang

Xiao Ma

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, MX, upon reasonable request.

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Associations Between Leisure Physical Activity and Mathematics Interest: A Longitudinal Analysis

Abstract

Keywords

Introduction

Materials and Methods

Sample

Ethics Permission

Dependent Variable

Independent Variables

Covariates

Methodology

Results

Discussion

Strength and Limitations

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iDs

Data Availability Statement

References