¹ Asst. Prof. III, Institute of Education, Arts, and Sciences, City College of Angeles, Angeles City, Philippines.

^{2,3,4,5,6,7,8,9} City College of Angeles, Angeles City, Philippines.

*             Correspondence: mlcelis@cca.edu

Keywords: AHKGA, Academic, National Ethical Guidelines, RA, PA, Health, Angeles City, Philippines

1. INTRODUCTION

The Active Healthy Kids Global Alliance (AHKGA) started the Global Matrix 4.0 initiative to evaluate how effectively countries are promoting youth involvement in PA, particularly by providing them more chances to participate in physical activity [1]. Every nation creates its own report card using a set process. The main outcome is the PA Report Card, a tool for communication to inform young people about the PA issue all around. It also aims to inform and influence supporters and stakeholders towards increasing young PA options [2]. Roughly 30% of the population are children and young people among the over 108 million people living in the archipelagic country of the Philippines. Young Filipinos’ lack of physical activity (PA) is shown by surveillance data to be disturbingly common [3]. Specifically, the 2019 Philippine Food and Nutrition Research Institute (FNRI) found that 84.6% of Filipino teenagers between the ages of 10 and 17 are not achieving the advised level of PA for health. The high incidence of physical inactivity among young Filipinos is a public health concern that merits great attention from local needs to have a more comprehensive knowledge of the PA status of children and adolescents in the Philippines [4]. Stakeholders and public health officials. Article XIV Section 19 of the 1987 Constitution states that the state shall foster sports programs and boost physical education, amateur sports, league competitions, and preparation for international competitions to promote excellence, teamwork, and self-discipline for the creation of a population that is healthy and informed [5]. In this study, the advocates call Athleticism a node to survey his references to sports and games in general. Especially in the creative domains of philosophy, science, and art, sport is addressed. Given this, one might argue that what athletes produce are movements, or new styles they create with the power to alter the sport and show new points of view on the human body in motion. The Academic self-efficacy is defined as perceived ability to learn or carry out tasks at specified levels in academic environments. Academic self-efficacy is based on Bandura’s social cognitive theory, which claims that behavioral, social/environmental, and personal variables all interactively affect one another. Individual agency, the belief that one can greatly affect major life events, requires self-efficacy [6]. Given that both positive psychology and self-efficacy emphasise agency and flourishing, they fit well together. Presented are studies including instructors and students as well as the concept of self-efficacy, which suggests that self-efficacy may be increased and influence self-regulation, motivation, and learning. The latter part of the chapter contains suggestions for further study as well as implications for education. The main objective is to increase the range of scholarly study on self-efficacy. The earlier study seemed to lack empirical evidence. Past study has not been very rigorous. Some unstudied results suggested that student athletes’ higher degrees of self-efficacy are a major predictor of their academic success. These results seem to be significant and need further investigation within the context of the Angeles City community, where no prior studies have been conducted [7]. These questions have to be experimentally studied as the findings will enable greater study on how various cultures’ views of athleticism and academic self-efficacy might produce different results. Most of previous studies have been qualitative and focused on the worldwide community; there has not been much local study on the topic, especially in the Angeles City area [8]. So yet, no study has directly tried to objectively evaluate the athletics and academic self-efficacy of communities, particularly those in Angeles City. Most of the limited empirical research done has focused only on the academic aspects of students in multinational communities. Another study found almost no correlation between self-efficacy and athleticism [9]. The community-based strategy has been mostly focused on different studies and could depend on the factors provided for further clarification and community support [10]. Understanding the empirical gap between athleticism and academic self-efficacy is essential for the whole development of community-based athletes. Understanding the academic challenges these athletes face and the influence sports has on their self-efficacy helps us to create customized solutions to better assist them [11]. The community as a whole, academic institutions, and sports leagues must all work to bridge this empirical gap. More study is absolutely required to grasp this link and to ensure the general progress and success of community-based athletes in Angeles City and beyond. The goal is to evaluate the athleticism and academic effectiveness of community based athletes as well as to find the factors that affect the correlation between athleticism and academic self-efficacy among community based athletes and the degrees of academic self-efficacy of community based athletes. Outline the athletic and personal profiles of community based athletes.

2. MATERIALS AND METHODS

Three (3) component questionnaires will be used in this investigation. The demographic profile and history are covered in Part 1. This study is meant to be a descriptive correlational one; the athlete will provide their histories in playing sports to assess their history in playing sports as an athlete. To collect the data required for this study, following the conclusion of the request letter for the study’s execution. The researchers want authorization from the relevant authorities to seek approval for the survey or study in order to get the data. The chosen individuals will answer the athletics and academic self-efficacy questionnaire. The researchers will also monitor and advise each responder during the whole procedure, so guaranteeing their timely completion, so evaluating the questionnaires. The researchers must guide, supervise, and track every single responder during the whole session and will do so according to collect data required for the study. Sampling Method and Sample This study emphasized community based athletes outside of sport. Give a basic summary of the people. Specifically, the researchers will be recruiting possible Respondents in Angeles City using purposive sampling. Describe the general local area of the people. The following selection criteria will help you attract responders and provide a consistent, believable sample. Age (must be 18 years old and above) Six months of being Community based athlete or more Must be community based athlete. Conversely, the following exclusion criteria can help to find possible study participants who ought not be included: A student living as one with physical limitations. A pregnant student. A student with asthma. This study will use a three (3) component questionnaire for component I. Part I covers the historical and demographic characteristics. Part 1 covers the demographic profile and history of the respondents. Knowing the respondents’ age, gender, physical activity history—sports, the researchers will address their demographic profile. This also covers the respondents’ height and weight. This will let the researchers evaluate the respondents more about their circumstances and capacity for conducting physical activities. The researchers designate Part 1 depending on the current literature review. The researchers modified the Athlete Engagement Questionnaire for Part II. With a Cronbach Alpha above 0.80, this survey Four aspects—confidence, vigour, determination, and enthusiasm—the Athlete Engagement Questionnaire (AEQ) gauges athlete involvement. Like the first edition, it uses a Likert-type answer system from 1 (almost never) to 5 (almost frequently). Using a 5-point Likert-type scale, from 1 (strongly agree) to 5 (strongly disagree), respondents are asked to indicate their emotions during the last three months. The AEQ scale was initially translated into Portuguese and then changed to English to reduce the discrepancies between the original and updated translations [12]. The researchers modified an Academic Self-Efficacy Scale for Part III. Face, content, and expert validation will be obtained to guarantee validity and reliability of the data collecting instrument to be employed. Member verification was done for Face Validity. A study by [13] defines face validity as the extent to which a test seems to assess the desired result. If most people believed the items on a test seemed to measure the things the exam was meant to evaluate, the test would have high face validity. A mathematics test containing questions calling for the test-taker to add and subtract numbers, for example, may be seen as having great face validity. At least on the surface, the exam questions seem to gauge what is being assessed. Examining the truth of findings, member checking—sometimes known as responder or participant validation—is a technique. Participants get findings or data back to confirm correctness and suitability with their experiences. Often mentioned among the validation techniques is member checking [14]. The study authors got CCA College Guidance and Formation Office clearance for Content Validity. According to [15], the content validity of an instrument reflects how well it captures every relevant feature of the construct it aims to assess in a test-like environment. A theoretical notion, issue, or idea—in this context, one that is usually not immediately measurable—is called a construct. For Expert Validity, the academics sought expert input from faculty members in charge of the subject topic. Expert Validity as per [16] this guarantees the measure is really assessing the construct, not other influences. Using a panel of “experts” familiar with the notion, one may investigate this kind of validity. The items may be looked at by the experts who can then say what each item’s intended measurement is. Students may be involved in this process; their comments are welcomed. The following quantitative data analysis techniques will be used to provide dependable outcomes after the goal quota is met: Descriptive – intends to characterize a segment of raw data using summary statistics, tables, and graphs. It also helps to arrange a sample’s traits such that one can see group trend. The techniques and basic concepts in this group have a very clear purpose in mind: to simplify data description and summarization. Usually, when we say “describe,” we imply either utilizing a visual or graphical representation of the data or calculating an index or number meant to summarize a certain characteristic of a measurement or variable [17]. It enables researchers to present data in a more logical way that facilitates understanding. Descriptive statistics use two methods to organize and define data. [18] Claims that inferential statistics is a technique used to evaluate how athletics and academic self-efficacy are connected by comparing the differences between groups receiving different treatments or interventions. This is to generalize, deduce, and forecast using the data. This will be used to examine the tables and assist the hypothesis of the research on the link between academic self-efficacy and athletics. Pearson correlation and regression analysis. The descriptive data for the respondents’ background, sports involvement, and academic self-efficacy were shown in terms of f and %. Using the Shapiro-Wilk Test, the distribution of the variables was evaluated for normality; results showed a non-normal distribution with a p-value under 0.05. This study will follow the policies and procedures set forth in the 2022 National Ethical Guidelines for study Involving Human Participants in conformity with RA 10532.The study will use quantitative data analysis techniques to provide reliable outcomes after meeting the goal quota. Descriptive statistics will be used to characterize raw data using summary statistics, tables, and graphs, allowing researchers to present data in a logical way. Inferential statistics will be used to evaluate the relationship between athletics and academic self-efficacy by comparing differences between groups receiving different treatments or interventions. Pearson correlation and regression analysis will be used to examine the tables and support the hypothesis. The study will follow the 2022 National Ethical Guidelines for study Involving Human Participants, adhering to RA 10532. Participants will be guaranteed confidentiality, anonymity, non-traceability, and privacy throughout the data collection process. Data will be kept physically and electronically, with identifying personal information coded uniquely for researchers, advisors, and data processors. Outcomes and information dissemination will be for academic use, with the CCA College Library hosting a published copy of the study report and public fora and academic conferences spreading key results.

3. RESULT & DISCUSSION

The necessary statistical methods were used to organize and analyses the data collected, hence showing the following notable results: The personal background characteristics of the respondents are shown in Table 1. The study shows demographic composition knowledge according on age, gender, height, weight, and BMI categories. Most of the respondents, 43.9% of the sample, are between 19 and 21 years old. While 18 years and under account for 25.5%, those 22 years and over make up 30.6%. Men make a notable majority at 65.0% while women make up 35.0% of the responses in terms of sex. The distribution indicates that the most frequent range is from 153cm to 164cm, with 36.3% falling into this group. Coming in at 43.9% are heights between 165cm and 176cm; next are those under 152cm at 12.7% and those 177cm and above at 7.0%. Conversely, those who weigh between 51kg to 55kg and 56kg to 60kg each make around 20.4% and 23.6% respectively. While those between 61kg to 65kg and 66kg and above each account for 13.4%, those weighing 50kg and less make up 29.3%. At 70.1%, most of the respondents fit into the “Normal” group in terms of BMI categorization. While “Overweight” and “Obese” people are less frequent at 8.9% and 1.9%, respectively, those categorized as “Underweight” make up 19.1%. The study of Table 1 shows a varied profile of the respondents overall depending on age, sex, height, weight, and BMI classifications. With a notable male presence, most of the responders are young people between 19 and 21 years old. While weight distribution is dispersed over many categories with a significant representation in the 51 kg to 60kg range, height distribution reveals a concentration in the mid-range categories. Reflecting a relatively healthy BMI distribution across the sample, most responders fall into the “Normal” BMI group. The 2011 data gathering included 520,102 total registered participants. Participation dropped sharply throughout adolescence. Young individuals, age 18 to 29, made up a larger proportion of male gamers than female participants. While a bigger proportion of people from non-metropolitan regions played from adolescence into adulthood, a higher proportion of those from metropolitan areas were engaged in the activity between the ages of 19 and 29.

Table 01: Personal Profile

Profile		N	%
Age
	18 years old and below	40	25.5 %
	19 years old – 21 years old	69	43.9 %
	22 years old and above	48	30.6 %
Sex
	Male	102	65.0 %
	Female	55	35.0 %
Height
	152cm and below	20	12.7 %
	153cm – 164cm	57	36.3 %
	165cm – 176cm	69	43.9 %
	177cm and above	11	7.0 %
Weight
	50kg and below	46	29.3 %
	51kg – 55kg	32	20.4 %
	56kg – 60kg	37	23.6 %
	61kg – 65kg	21	13.4 %
	66kg and above	21	13.4 %
BMI
	Normal	110	70.1 %
	Underweight	30	19.1 %
	Overweight	14a	8.9 %
	Obese	3	1.9 %

The academic background characteristics of the respondents are thoroughly shown in Table 2. The data is divided into three important categories: year/grade level, school attended, and kind of school, academic standing, and latest grade average. Grade 12 students make up the most share at 45.9% in year level distribution, followed by 1st year college students at 12.1%. While Grade 11 and 4th Year College students each account for 8.3%, 2nd year college and 3rd year college students make up 15.3% and 10.2% respectively. Grade 12 pupils have major representation in the statistics, followed by first year college students and other many college levels. Of the schools attended, Angeles City National High School stands out most at 22.3%; Systems plus College Foundation follows at 15.9%. City College of Angeles and Francisco G. Nepomuceno Memorial High School both reveal notable presence at 15.3% and 11.5% respectively. Of the respondents, 59.2% attended public schools and 40.8% attended private ones, hence the kind of school attended shows a little majority. The distribution among various schools reveals a concentration in both public and private educational institutions, with significant attendance at Angeles City National High School and Systems plus College Foundation. Academically, most of the respondents—94.9% of whom are categorized as regular students—are regular. Of irregular pupils, 5.1% are regular. Finally, regarding the most recent grade average, the distribution indicates that most of the respondents (52.9%) have a grade average of 89 or lower, followed by 90-93 (40.8%) and 94 and above (6.4%). Reflecting a consistent academic standing, most of the respondents are categorized as regular students. Academically, a large percentage of the respondents keep grade averages in the 89 and lower level; many more additionally score between 90 and 93. All things considered, Table 2 shows the various academic backgrounds of the respondent sample, hence representing distinct educational stages, institutions, academic statuses, and performance levels. These revelations provide insightful background for grasping the educational profile of the respondents and its consequences for the conclusions of the study claim that participation in high school sports raised several Grade 12 and postsecondary results including academic standing, course selection, homework, educational and career goals, self-worth, college applications, enrolment in following schools, and final educational achievement. Participation in sports is thought to raise identification with and dedication to the school and school values, hence offsetting the effects of participation, particularly for academically defined outcomes not directly linked to sports activities. Consistent with this Identification/Commitment Model, extramural sports—and to a lesser degree team sports—had more positive advantages than intramural and solitary sports.

Table 02: Educational Background

	Profile	N	%
Year/Grade Level
	Grade 11	13	8.3 %
	Grade 12	72	45.9 %
	1st year college	19	12.1 %
	2nd year college	24	15.3 %
	3rd year college	16	10.2 %
	4th year college	13	8.3 %
School
	Systems Plus College Foundation	25	15.9 %
	Republic Central Colleges	11	7.0 %
	Angeles City National High School	35	22.3 %
	City College of Angeles	24	15.3 %
	Systems Technology Institute	8	5.1 %
	Francisco G. Nepomuceno Memorial High School	18	11.5 %
	Angeles City National Trade School	17	10.8 %
	Holy Angel University	13	8.3 %
	National University	1	0.6 %
	AMA Computer College	5	3.2 %
Type of School
	Private	64	40.8 %
	Public	93	59.2 %
Academic Status
	Regular	149	94.9 %
	Irregular	8	5.1 %
Latest Grade Average
	89 and below	83	52.9 %
	90 – 93	64	40.8 %
	94 and above	10	6.4 %

 

Table 3 shows the athletic background characteristics of the respondents, emphasizing their involvement in sports, sources of influence, varsity level, formal training, competition experience, awards obtained, and training frequency. Among the respondents, basketball stands out as the most popular sport in terms of involvement; 54.1% are actively engaged; volleyball follows at 31.8%. Representing 10.2%, badminton is also significant; other games like chess, sepak takraw, and table tennis have little presence. Friends are the most important source at 59.2%, followed by family members at 28.7%, with the other factors affecting their athletic endeavors differing greatly. At 7.0% and 5.1%, respectively, coaches and well-known athletes contribute less. Of those who answered, 15.3% are engaged in varsity athletics; the others, 84.7%, are not. Of the total, 58.6% have had official training in their particular sports, hence stressing a methodical approach to skill development.
Of those polled, 75.2% had participated in organized sporting activities, hence showing active involvement in competitive environments. Furthermore, 63.7% have been acknowledged as awards in their particular sports, so indicating accomplishments in their athletic pursuits. Finally, with respect to training habits, 54.8% of those polled actively participate in training sessions, so highlighting a dedication to preserving and enhancing their athletic abilities. All things considered, Table 3 offers a picture of the various and active athletic histories of the respondents showing their engagement levels, sources of inspiration, competitive experiences, and dedication to continuous skill improvement. claims adjustments are conceivable given basketball’s very dynamic team sport. Knowing how to use tactical information is insufficient; one must also learn, grow, and assess it. Although analyzing tactical data is vital, few studies look closely at how training is used in actual situations to improve players’ tactical performance. This offers possibilities for assessing and studying the tactical performance of basketball players using a consistent, valid instrument. A study of many studies reveals the relevance of tactical performance as well as the benefits and drawbacks of the tactical skill measurements formerly in use. Thus, one may argue that evaluating tactical knowledge calls for a consistent and simple instrument. A good tool connected to game performance and can change basketball plays at all levels helps one to identify tactical understanding and grow players.

Table 03: Athletic Background

Profile		N	%
Sports
	Volleyball	50	31.8 %
	Basketball	85	54.1 %
	Badminton	16	10.2 %
	Chess	2	1.3 %
	Sepak Takraw	1	0.6 %
	Others	2	1.3 %
	Table Tennis	1	0.6 %
Influence
	Friends	93	59.2 %
	Family	45	28.7 %
	Famous Athlete	8	5.1 %
	Coach	11	7.0 %
Varsity
	Yes	24	15.3 %
	No	133	84.7 %
Formally Trained
	Yes	92	58.6 %
	No	65	41.4 %
Competed
	Yes	118	75.2 %
	No	39	24.8 %
Awardee
	Yes	100	63.7 %
	No	57	36.3 %
Training Regularly
	Yes	86	54.8 %
	No	71	45.2 %

Table 4 reveals the respondents’ degree of athleticism. The study shows that most of the respondents—64.3%—are in the Very Low group, suggesting a lesser degree of athleticism. Of those surveyed, 30.6% are classified as Low in athleticism, implying a moderate degree of physical activity and fitness involvement. By comparison, a smaller percentage of people show more athleticism. Of the total, 3.8% are classified as High, suggesting a more active and involved attitude towards physical fitness. Moreover, only 1.3% of responders qualify as Very High, indicating an uncommon and extraordinary degree of athleticism within the sample. All things considered, Table 3 draws attention to the spread of athleticism levels across the responder group, stressing a prevalence of lower to moderate degrees of physical activity and fitness involvement. A study emphasized the particular ways in which sports assist college students become more physically fit. It also shows how physical education influences athlete drive. Including sports into physical education classes at higher education universities has been shown to greatly increase students’ degrees of physical fitness. A lack of physical exercise was shown to be one of the factors influencing the increase in student morbidity and poor degree of physical development. These factors combine to create a student’s degree of efficiency and physical fitness, which are vital indicators of their professional training. Application of different strength-training methods—such as the method of unsatisfactory efforts with a normalized number of repetitions, the method of unsatisfactory efforts with the maximum number of repetitions, the dynamic efforts method, the shock method, the static-dynamic method, the circular training method, and the game method—helps all physical qualities develop. Students’ indices of general physical health suffer since they almost never engage in sports on their own during holidays. The poll results show that only students at higher education institutions are actively engaged in physical culture.

Table 04: Athleticism Level of the Respondents

Profile		N	%
	Very Low	101	64.3 %
	Low	48	30.6 %
	High	6	3.8 %
	Very High	2	1.3 %

Table 5 shows the four degrees of academic self-efficacy of the respondents described. Of the respondents, 77.7% said they have Low degrees of academic self-efficacy, which the data shows are a clear majority. This implies a feeling among the sample that their capacity to study could be restricted or unknown. By comparison, 20.4% of respondents show High degrees of academic self-efficacy, suggesting a more certain view of their academic potential. Of a lesser percentage, 1.3%, say Very Low levels of academic self-efficacy, suggesting a low confidence in their academic skills. Only 0.6% of those polled also say Very High degrees of academic self-efficacy, suggesting a remarkable trust in their academic knowledge and capabilities. Table 5 offers a general picture of the distribution of academic self-efficacy levels within the respondent group, hence stressing different levels of confidence in their academic ability. It Claim that since more students in recent decades have to balance other major obligations outside their education, university students are under increasing pressure to excel in their courses. Children may also succeed when they have self-assurance. This study examined the stress and academic self-efficacy levels of 305 Australian Teacher Education students using a self-reported survey. The results indicate that younger and female students might be more anxious than their peers and have less academic self-efficacy. Students in higher education may consider concentrating help or intervention on certain student populations to boost their self-confidence in their capacity to finish academic tasks. We will discuss the consequences and recommendations for further study and use.

Table 05: Academic Self-Efficacy Level of the Respondents

Profile		N	%
	Very Low	2	1.3 %
	Low	122	77.7 %
	High	32	20.4 %
	Very High	1	0.6 %

Spearman’s rho test was used to investigate the association between athleticism and academic self-efficacy among the respondents; the results are summarized in Table 6. The study sought to find if a relationship between degrees of athletics and academic self-efficacy existed. The results show no meaningful link among the respondents between athleticism and academic self-efficacy. The correlation coefficient was -0.020 and the p-value was 0.801. This p-value implies that the findings did not reject the null hypothesis (Ho), hence showing no statistically significant link between academic self-efficacy and athleticism. Thus, based on these findings, one may infer that the degree of athleticism shown by the respondents does not notably affect their views of academic self-efficacy. This implies that while personal growth depends on athletics and academic self-efficacy, they seem to function separately within the group under study.

Table 06: Test of Correlation between the Respondents’ Athleticism and Academic Self-Efficacy

	Athleticism	Spearman’s Rho Test	Decision
Academic Self-Efficacy	Correlation Coefficient	-0.020	No Correlation
	p-value	0.801	Failed to Reject Ho

 

Table 6.1: Normality Result

	p value
Athleticism	<.001
Academic Self-Efficacy	<.001

 

Spearman’s rho Test will be used to examine the association between the athleticism and academic self-efficacy of the respondents, with p-value less than or equal to .05 deemed important. The acquired correlation coefficient value was interpreted using the strength of correlation.

Table 6.2:  Dancey and Reidy Strength of Correlation

Correlation Coefficient Value	Direction and Strength of Correlation
±1.00	Perfect (+/-) correlation
±0.70 to ±0.99	Strong (+/-) correlation
±0.40 to 0.69	Moderate (+/-) correlation
±0.10 to 0.39	Weak (+/-) correlation
±.00 to 0.09	No correlation

The researchers then set the following based on their data analysis: The aim of the present study was to determine if sports activity influences students’ views of their academic ability and to investigate the link between athleticism and academic self-efficacy among the participants. The findings of our study indicate that, as shown by their participation in sports, there is no meaningful link between athleticism and academic self-efficacy ratings of respondents. The correlation coefficient was -0.020 with a p-value of 0.801. This p-value suggests no statistically significant link between athleticism and academic self-efficacy, suggesting the findings did not effectively reject the null hypothesis (Ho). This result corresponds to other prior studies that also found no notable link between sports and academic self-efficacy. For instance, concludes that engagement in sports diverts attention from studies since popularity is the primary goal of involvement rather than academic achievement. With other academics trying to defend or reject his assertions, he has been the benchmark for study in this sector for the last fifty years. Similarly, it seemed that participation in sports did not correspond with greater high school academic performance if a study controlled for first variance between individuals. Thus, depending on these findings, one plausible explanation may be that the degree of athleticism shown by the participants did not especially affect their views of academic self-efficacy. Examining 111 Arizona high school students, found a robust link between sports involvement and better GPA via the variable of self-efficacy. They also found, however, that athletes exhibited more self-efficacy than non-athletes; the source or cause of this difference remained unknown. Athletic engagement in the study was limited to school-sponsored sports backed by a qualified coach; it was clear there was no relationship between the athletic and academic levels of self-efficacy. This implies that while personal growth depends on athletics and academic self-efficacy, they seem to function separately within the studied group. Furthermore, one must take into account the intricacy of elements supporting academic self-efficacy. Although everything may still be true, this study offered no proof that any of these elements improves academic achievement. The study emphasized the need of include personal and contextual elements in the knowledge of the link between sports and academic self-efficacy and recommends further investigation on gender differences. Instead of supposing a straight link between physical involvement and self-efficacy, teachers and lawmakers should emphasise enhancing academic self-efficacy by means of customized teaching strategies. Studies show that athletics and academic self-efficacy are different but important components of student development. Future study should investigate the multifaceted interaction between self-efficacy beliefs.

Discussion

The purpose of the current study was to ascertain whether sports activity affects students’ perceptions of their academic competence and to explore the relationship between athleticism and academic self-efficacy among the participants [19]. Our study’s conclusions show that there is no significant relationship between respondents’ academic self-efficacy scores and athleticism, as demonstrated by their involvement in sports. With a p-value of 0.801, the correlation coefficient was -0.020 [20]. There is no statistically significant relationship between athleticism and academic self-efficacy, according to this p-value, which indicates that the results did not successfully reject the null hypothesis (Ho). This outcome is in line with a number of earlier research projects that likewise did not discover any significant association between athletics and academic self-efficacy. A study [21] comes to the conclusion that involvement in athletics distracts from academics since popularity is the main objective of participation rather than academic success. For the past 50 years, he has served as the standard for research in this field, with other scholars attempting to support or refute his claims. Likewise, it seemed that involvement in athletics did not correlate with higher academic achievement in high school if a study adjusted for initial variation across students. Therefore, based on these results, one possible explanation could be that the level of athleticism displayed by the respondents does not significantly influence their perceptions of academic self-efficacy. Recent study [22] examined 111 high school students in Arizona and discovered a strong correlation between athletic participation and higher GPA through the variable of self-efficacy. They also discovered that athletes had higher levels of self-efficacy than non-athletes, though it was unclear from what source or for what reason. The study’s athletic participation involved only school-sponsored sports that were supported by a certified coach through the school; it was also evident that there was no correlation between the athletic and academic levels of self-efficacy [23]. This suggests that while athleticism and academic self-efficacy are important aspects of personal development, they appear to operate independently within the surveyed population. Moreover, it is essential to consider the complexity of factors that contribute to academic self-efficacy. While all of this may still be true, there is no evidence in this study to suggest that any of these factors leads to improved academic performance [24]. The study highlights the importance of considering individual and environmental factors in understanding the relationship between athletics and academic self-efficacy, and suggests future research on gender disparities. Educators and legislators should focus on improving academic self-efficacy through tailored instructional approaches rather than assuming a direct correlation between physical engagement and self-efficacy [25]. Research indicates academic self-efficacy and athleticism are distinct, yet significant aspects of student growth. Future studies should explore the complex relationship between self-efficacy beliefs.

 4. CONCLUSION

Aiming to know how involvement in sports shapes views of academic ability, this study sought to explore the interaction between athleticism and academic self-efficacy among community-based athletes. Several important results that help us to better know these correlations have come from a thorough examination of data gathered from a varied sample of community-based athletes.
Among community-based athletes, the main result of this study is the lack of a notable link between athleticism and academic self-efficacy. Participation in sports does not seem to increase people’s confidence in their academic skills contrary to first assumptions and common notions.
The lack of link shown in our study emphasized the different areas of self-efficacy that students and athletes interact with. Although athletes may show great degrees of discipline, tenacity, and collaboration within their sporting activities, these qualities do not automatically transfer into increased confidence or perceived ability in academic assignments. Ultimately, while athleticism is still a significant factor in personal growth among community-based athletes, its direct impact on academic self-efficacy is minimal. This study added to the larger conversation on the intricate interaction between sports involvement and academic results by advocating more investigation and strategy improvement to assist the whole growth of student-athletes. Here are some suggestions that may be drawn from the results showing no meaningful link between athleticism and academic self-efficacy among community-based athletes. Consider doing longitudinal studies to track changes in academic self-efficacy and sports participation over time. Longitudinal design studies might highlight the interactions and changes between these factors as athletes and students go through various developmental stages. Qualitative study: Use qualitative methods such as focus groups and interviews to get knowledge on the real experiences of community-based athletes with regard to their academic self-efficacy. The rich, contextual knowledge of qualitative data may improve quantitative outcomes. Look into how outcomes differ based on the kind of sport—individual vs. team sports, for instance—to determine if athletic participation has a different influence on academic self-efficacy. This can help to identify certain needs or qualities of sports that might be more beneficial for developing academic confidence. The study suggested a program to the SK Federation to assist the community athlete in academics and athletics terms. Raising academic self-efficacy requires using individualized support strategies as student-athletes are so different. This might include academic coaching and flexible scheduling as well as customized therapies supporting athletes’ academic goals and difficulties. Focusing on the development and recognition of transferable skills—such as discipline and teamwork—acquired through sports may help to enhance academic performance and general student success. Coaches and educators should help student-athletes understand and use their skills in academic settings. The researchers in this study did a survey across many Angeles City barangay. Finding survey participants was difficult for the researcher given the small number of community athletes in Angeles City, so he turned to the SK Federation for assistance. Researchers need to get data from 200 respondents, hence doing study in Angeles City presents several challenging circumstances. While some people disregard survey questions, others complete the survey.

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5. Carter, V. (2021). The Relationship between Self-Efficacy, Self-Compassion, and Anger among Evangelical Men Ages Thirty to Forty(Doctoral dissertation, Midwestern Baptist Theological Seminary).
6. MacNeil, H., Schoonmaker, M., & McAdam, M. (2022). Accelerating alienation: gender and self-efficacy in the accelerator context. International Journal of Entrepreneurial Behavior & Research, 28(8), 2083-2102.
7. Dennard, D. (2024). Impact of Self-Efficacy on College Degree Attainment Amongst Black First-Generation College Students from South Los Angeles, California(Doctoral dissertation, Alliant International University).
8. Diop, S. M. (2025). Understanding the Role of Athlete Leaders in Peacebuilding: An Exploration of Characteristics and Contributions(Doctoral dissertation, Pepperdine University).
9. Harmie Jay C. Jopia (2025). Differentiated Instruction of Teachers and Sense of Coherence of Students. Dinkum Journal of Social Innovations, 4(01):34-42.
10. Langley, T. J. (2022). The Impact of Traditional, Black, and Athlete-based Masculinity on Psychological Help-Seeking Among Black Men College Athletes(Doctoral dissertation, Auburn University).
11. McVay, S. S. (2024). Examining the Buffering Effect of Self-Continuity on the Association Between Peer Victimization and Self-Esteem Among Brazilian Adolescents(Master’s thesis, University of Nebraska at Omaha).
12. Karmakar, M. D. (2024). Collective Mindset: Sociological Insights into Sports Psychology. OrangeBooks Publication.
13. Lochbaum, M., & Sisneros, C. (2024). Situational and Dispositional Achievement Goals and Measures of Sport Performance: A Systematic Review with a Meta-Analysis. Sports, 12(11), 299.
14. Iwasaki, S., Fry, M. D., & Hogue, C. M. (2021). Mindful engagement mediates the relationship between motivational climate perceptions and coachability for male high school athletes. Journal of Clinical Sport Psychology, 16(3), 234-253.
15. Sperber, T. (2024). Climate Change and the Olympic Movement: Exploring, Explaining and Comparing the bi-directional relationship of Climate Change within the Organized Sport Structures in Aotearoa New Zealand, Brazil, Germany, and South Africa.
16. Norquist, T. M. (2023). Illuminating the Black Male Athlete Experience at Community College(Doctoral dissertation, San Diego State University).
17. Muñoz-Helú, H., Reynoso-Sánchez, L. F., Cruz-Morales, K. N., Salazar-C, C. M., & Mataruna-Dos-Santos, L. J. (2025). In the perception of the Olympic movement and gender equity in sport, are gender and sport practice determining factors? Frontiers in Sports and Active Living, 7, 1564617.
18. Holmes, G. (2022). ” My Body Goes Numb a Lot of the Time”: Examining High School Student-Athlete Experiences Using Youth Participatory Action Research and Embodied Culturally Relevant Pedagogy(Doctoral dissertation, University of San Francisco).
19. Streetman, A. (2024). Empowering Women to Be More Physically Active Through High-Intensity Resistance Training. Kansas State University.
20. Azadi, H., Meshkati, Z., & Rice, S. (2024). The effects of training conditions on athletes’ mental health throughout the COVID-19 pandemic: psychometric validation of the Persian Athlete Psychological Strain Questionnaire. Apunts Sports Medicine, 59(222), 100437.
21. Krieger, J. (2024). The Impact of Professional Sport on Community: Fourteenth International Conference on Sport & Society.
22. Harrison, T. (2024). The Impact of Experiential Learning on Perceived Learning and Self-Efficacy in Literacy for Elementary Education: A Quantitative, Causal-Comparative Study(Doctoral dissertation, Liberty University).
23. FOSSI, L. (2024). EXPLORING THRIVING IN SPORT: A JOURNEY THROUGH RESEARCH AND PRACTICE.
24. Bidder, B. M. (2022). Leadership in Resource Starved Communities: Rugby Coach Narratives of Athletics to Develop Pre-Trauma Resilience in Emerging Adults(Doctoral dissertation, University of Charleston-Beckley).
25. Martin, U. (2021). The interplay of academic underperformance and affluence among middle school students. Immaculata University.