To Study or to Play: Assesing the Self-Regulated Learning Habits of the Student-Athletes

Introduction

Student-athletes continually face the challenge of balancing rigorous academic requirements with demanding athletic schedules. This dual commitment often creates tension between time spent on academic tasks and time allocated for training, competition, and recovery. As universities place increasing expectations on both academic success and athletic excellence, student-athletes must develop effective strategies that enable them to maintain competitive performance in both domains. In the Philippines, recent studies emphasize the growing importance of self-regulated learning among student-athletes [1]. Found a strong relationship between self-monitoring, self-judgment, and sports preparedness among college athletes in Region III. Similarly, [2] reported that student-athletes with higher self-efficacy and self-regulation skills tend to perform better in sports competitions. These findings highlight the critical role of self-regulatory mechanisms in enabling athletes to manage the demands of academics and sports simultaneously.

Despite increasing recognition of the importance of self-regulated learning, most studies focus on either academic learning or sports performance independently. Limited research has explored how student-athletes regulate learning across both domains simultaneously. International literature suggests that self-regulation skills developed in sports may transfer to academic learning, and vice versa. However, this cross-domain relationship remains underexplored in the Philippine context, particularly among student- athletes from Jose Rizal Memorial State University. This study therefore aimed to examine how student-athletes from Jose Rizal Memorial State University-Dipolog Campus, Main Campus, and Katipunan Campus apply self-regulated learning strategies in managing their academic and athletic responsibilities.

Methods

Research Design

This study employed a quantitative-descriptive research design to examine the self-regulated learning habits of student-athletes. The design allowed the researchers to measure the extent to which student-athletes practice goal setting, time management, and metacognitive strategies. The descriptive approach was appropriate because the study aimed to describe existing conditions rather than manipulate variables.

Research Respondents

Table 1 presents the respondents, consisting of 40 student-athletes from three campuses of Jose Rizal Memorial State University who were actively participating in varsity sports programs.

Table 1:Research Respondents.

Research Instrument

The primary instrument used in this study was a researcher- developed and adapted survey questionnaire designed to assess the self-regulated learning habits of student-athletes. The instrument consisted of two parts.

Part I gathered demographic information about the respondents, including sex and sports event participation.

Part II measured self-regulated learning behaviors across three major dimensions:

1. Goal setting

2. Time management

3. Metacognition

The items were measured using a five-point Likert scale ranging from:

1 - Never

2 - Rarely

3 - Sometimes

4 - Often

5 – Always

The questionnaire was adapted and enhanced from existing validated instruments on self-regulated learning, including studies on goal setting [3], time management [4], and metacognitive strategies [5]. Modifications were made to ensure that the items were contextually relevant to student-athletes while maintaining alignment with established theoretical constructs of SRL.

Validation of the Instrument

To ensure the validity and reliability of the research instrument, the questionnaire underwent content validation by a panel of experts consisting of a research adviser, a measurement and evaluation specialist, and a faculty member with expertise in sports science and student-athlete development.

Each expert evaluated the instrument based on the following criteria:

• Relevance to research objectives

• Clarity of wording

• Consistency of items

• Alignment with self-regulated learning constructs

Suggestions from the panel were incorporated to refine the questionnaire and improve item clarity and structure. A pilot test was conducted among twenty (20) student-athletes who were not included in the main study sample. Reliability analysis using Cronbach’s Alpha produced a coefficient of 0.97, indicating excellent internal consistency. This result confirmed that the instrument was reliable and suitable for use in the actual data collection.

Validation and Reliability

The questionnaire underwent expert validation and pilot testing among 20 student-athletes. The reliability test yielded a Cronbach’s Alpha of 0.97, indicating excellent internal consistency.

Data Gathering Procedure

Data were collected through face-to-face surveys and online forms distributed through Messenger. Respondents were given clear instructions and sufficient time to answer the questionnaire.

Statistical Treatment of Data

The data gathered from the questionnaires were analyzed using appropriate statistical tools.

Frequency counts and percentages were used to determine the demographic profile of the respondents.

Weighted mean was used to determine the level of learning habits of student-athletes in terms of goal setting, time management, and metacognition.

where:

f= frequency of responses

w = assigned weight

N = total number of respondents

The computed means were interpreted using corresponding verbal descriptions to describe the level of self-regulated learning habits. To determine whether a significant relationship existed between the respondents’ profile variables and their learning habits, the Pearson Product-Moment Correlation Coefficient (r) was employed.

Where:

• and represent the variables being correlated

• represents the number of paired scores

• Σ xy represents the sum of the product of paired values

• Σ x and Σ y represents the sums of individual scores

• Σ x2 and Σ y2 represents the sum of squaresd values

This statistical test allowed the researchers to determine whether significant relationships existed between demographic variables and self-regulated learning habits among student-athletes.

Results and Discussion

Profile of the Respondents

Table 2 presents the distribution of the respondents according to sex. The results indicate that the respondents were equally distributed, with 20 male student-athletes (50%) and 20 female student- athletes (50%). This equal representation suggests that both sexes were adequately represented in the study, allowing for balanced perspectives regarding self-regulated learning habits among student-athletes.

Table 2:Profile of the Respondents in Terms of Sex.

The balanced gender distribution strengthens the reliability and validity of the study findings because it minimizes potential bias that could arise from gender imbalance. Having equal representation ensures that the learning habits assessed in this research reflect the experiences of both male and female student-athletes within the university athletic programs. Moreover, this distribution indicates that the institution provides comparable opportunities for sports participation regardless of gender. Previous studies highlight that gender may influence learning behaviors and study habits. For instance, Pujianto et al. (2024) reported that female student-athletes often demonstrate stronger study discipline and academic engagement compared with their male counterparts. Consequently, the equal representation of male and female respondents in the present study allows for more reliable interpretations of learning behaviors without overrepresenting a particular group.

Table 3 presents the distribution of respondents according to the sports events in which they participated. The results show that the respondents were involved in five different sports disciplines. Basketball, volleyball, and sepak takraw each had 10 respondents, representing 25% of the total sample. Meanwhile, futsal and football each accounted for 5 respondents or 12.50%. The findings indicate that the majority of respondents participate in ball-related team sports. Participation in team sports often involves intensive training schedules, regular team practices, and competitive events that require effective time allocation between academic and athletic commitments. Consequently, student-athletes in these sports may experience increased demands in managing academic responsibilities alongside training and competitions.

Table 3:Profile of the Respondents in Terms of Sports Event Participated.

The inclusion of student-athletes from multiple sports disciplines improves the generalizability of the study findings. Different sports may involve varying levels of physical intensity, practice frequency, and competition schedules, which can influence how athletes regulate their learning behaviors [6]. Emphasized that athletes’ academic routines and learning efforts may fluctuate depending on the intensity and schedule of their sport. Therefore, the diversity of sports represented in this study provides a broader perspective on how self-regulated learning operates across different athletic contexts.

Learning Habits of Student-Athletes

Table 4 presents the learning habits of student-athletes in terms of goal setting. The results reveal that the respondents frequently engage in goal-setting behaviors, with an overall weighted mean of 4.10, interpreted as “Often.” Several indicators reached the “Always” level, including planning how to balance class schedules and training sessions (WM = 4.33), keeping track of academic and athletic goals (WM = 4.25), and setting personal deadlines for assignments ahead of practices or competitions (WM = 4.28).

Table 4:Learning Habits of Student-Athletes in Terms of Goal Setting.

These results indicate that student-athletes actively establish structured goals that help them manage competing academic and athletic responsibilities. Goal setting appears to function as a strategic planning mechanism that enables them to organize tasks, maintain motivation, and sustain focus during demanding sports seasons. The findings suggest that student-athletes consciously apply goal-setting strategies to regulate both academic performance and sports commitments. By setting clear objectives and monitoring progress, they are able to prioritize tasks and remain disciplined despite intensive training schedules. This behavior reflects strong elements of self-regulated learning, where individuals actively control and direct their learning processes.

These results support the findings of Aydan (2025), who emphasized that goal setting acts as a motivational driver that encourages learners to maintain effort and persistence in completing academic tasks. Similarly, [7] found that structured goal-setting practices significantly enhance learning engagement and behavioral regulation. Thus, the high level of goal-setting behavior observed in this study confirms its importance as a key component of self-regulated learning among student-athletes. Table 5 presents the learning habits of student-athletes in terms of time management. The results indicate that respondents frequently practice effective time-management strategies, obtaining an overall weighted mean of 3.99, interpreted as “Often.” The highest-rated behaviors include following a daily schedule that balances academic and athletic activities (WM = 4.30) and maintaining class attendance despite fatigue from training (WM = 4.28).

These findings suggest that student-athletes regularly engage in planning and prioritization behaviors that allow them to manage academic responsibilities while maintaining athletic commitments.

Effective time management enables them to allocate sufficient time for studying, attending classes, participating in team practices, and maintaining physical recovery. The relatively lower rating for the use of planners or mobile applications (WM = 3.55) indicates that while student-athletes manage their schedules effectively, they may rely more on personal discipline rather than structured digital planning tools. This suggests an opportunity for institutions to introduce structured planning systems or digital productivity tools that could further enhance time-management efficiency.

Table 5:Learning Habits of Student-Athletes in Terms of Time Management. Learning Habits of Student-Athletes in Terms of Time Management.

The results are consistent with the findings of [4], who emphasized that effective time-management practices are closely associated with improved academic achievement and student well-being. Likewise, [8] reported that self-regulated learners tend to structure their study schedules strategically to maximize productivity. In the context of athletics, [9] further emphasized that student-athletes benefit significantly from structured scheduling strategies that allow them to manage demanding training routines alongside academic responsibilities. Table 6 presents the learning habits of student-athletes in terms of metacognition. The overall weighted mean of 3.96, interpreted as “Often,” indicates that respondents regularly engage in reflective thinking and self-monitoring of their learning processes. The highest-rated statement involved comparing academic and athletic performance to determine whether one affects the other (WM = 4.35), suggesting strong awareness of how different responsibilities interact.

Table 6:Learning Habits of Student-Athletes in Terms of Metacognition.

These results indicate that student-athletes demonstrate considerable reflective awareness regarding their academic performance and learning strategies. Through metacognitive processes such as evaluating study methods, identifying weaknesses, and recognizing distractions, student-athletes actively regulate their learning behaviors. Furthermore, the tendency to relate sports discipline to academic effort suggests the transfer of self-regulatory skills between athletic and academic domains. Such reflective practices allow student-athletes to adapt learning strategies, improve problem-solving skills, and maintain resilience despite academic and athletic pressures.

These findings support the work of [5], who emphasized that metacognitive strategies strengthen planning, monitoring, and evaluation processes in learning. Similarly, [10] reported that athletes who demonstrate high metacognitive awareness tend to perform better both academically and athletically due to their ability to evaluate and adjust learning strategies effectively. Table 7 presents the results of the correlation analysis examining the relationships among the three dimensions of learning habits. The results indicate significant positive relationships among goal setting, time management, and metacognition. The computed correlation coefficients range from 0.68 to 0.75, all with p-values less than 0.05, leading to the rejection of the null hypothesis.

Table 7:Significant Relationship Between the Learning Habits of Student-Athletes.

The strong relationship between goal setting and time management (r = 0.72) suggests that student-athletes who clearly define their academic and athletic goals are more likely to organize and allocate their time effectively. Clear goals provide direction and help athletes prioritize academic responsibilities alongside training commitments. The relationship between goal setting and metacognition (r = 0.68) indicates that student-athletes who establish clear goals are also more likely to reflect on their learning processes and evaluate their performance. Goal setting encourages continuous monitoring of progress and promotes reflective thinking about strategies used to achieve both academic and athletic objectives.

The strongest correlation was observed between time management and metacognition (r = 0.75). This suggests that students who effectively organize their schedules also demonstrate stronger reflective awareness about their learning strategies and academic progress. Metacognitive awareness allows student-athletes to evaluate how time constraints, fatigue, and distractions influence their academic performance, leading them to adjust their strategies accordingly. These findings support Zimmerman’s Self-Regulated Learning Theory, which proposes that planning, monitoring, and reflection function as interconnected processes within a cyclical learning system. Similarly, [11,12] emphasized that metacognitive strategy use is strongly associated with effort regulation and time-management behaviors. Lourenço (2024) further explained that these self-regulated learning components interact to enhance academic performance and adaptive learning behaviors.

Summary of Findings

Based on the analysis of data, the following findings were obtained:

1. The respondents were equally distributed according to sex, with 50% male and 50% female student-athletes.

2. The respondents participated in five sports events, with the highest participation in basketball, volleyball, and sepak takraw.

3. Student-athletes demonstrated a high level of goal-setting habits with an average weighted mean of 4.10, indicating frequent use of goal-oriented strategies.

4. Student-athletes also demonstrated strong time-management skills with an average weighted mean of 3.99, indicating that they regularly organize their academic and training schedules.

5. The respondents exhibited metacognitive learning behaviors with an average weighted mean of 3.96, suggesting that they frequently reflect on and evaluate their learning strategies.

6. Significant positive relationships were found among goal setting, time management, and metacognition, indicating that these learning habits reinforce each other in supporting self-regulated learning among student-athletes.

Conclusion

Based on the findings of the study, the following conclusions were drawn:

1. Student-athletes in Jose Rizal Memorial State University demonstrate effective self-regulated learning behaviors that enable them to manage the demands of academic and athletic responsibilities.

2. Goal setting, time management, and metacognition are essential components of self-regulated learning that help student- athletes maintain academic engagement despite rigorous training schedules.

3. The significant relationships among the three learning habits confirm that self-regulated learning operates as an interconnected process in which planning, monitoring, and reflection collectively support academic and athletic performance.

4. The presence of strong self-regulated learning behaviors among student-athletes indicates that these skills contribute to improved discipline, motivation, and academic persistence.

Recommendations

Based on the conclusions of the study, the following recommendations are proposed:

1. Universities may develop academic support programs specifically designed for student-athletes to strengthen self-regulated learning strategies.

2. Coaches and academic advisers should encourage student-athletes to practice structured goal setting and reflective learning.

3. Institutions may introduce time-management workshops and digital planning tools to help student-athletes organize academic and athletic commitments.

4. Future researchers may conduct larger studies involving multiple universities to further explore self-regulated learning among student-athletes.

5. Additional research may investigate the relationship between self-regulated learning and academic performance indicators such as GPA.

Data Availability Statement

The data supporting the results of this study can be obtained from the researchers upon reasonable request. All collected data are kept confidential and were used strictly for academic purposes to ensure the privacy and protection of the participants.

Ethical Statement

This study adhered to ethical standards for research involving human participants. Permission was obtained from the Jose Rizal Memorial State University administration, the Office of Student Affairs, and the designated sports coordinators. Participants were informed about the study’s purpose, their voluntary participation, and their right to withdraw at any time. Responses were kept anonymous and confidential, and all data were used solely for academic purposes. The study ensured the participants’ rights, privacy, and well-being were fully protected, with no physical, psychological, or academic risks involved.

Funding Information

This research received no external funding and was conducted solely for academic purposes. All expenses related to the study were covered by the researchers.

Acknowledgement

The researchers sincerely thank Jose Rizal Memorial State University - Dipolog Campus for the opportunity to conduct this study. Special gratitude is extended to the respondents for their cooperation and to their research adviser, Mark Anthony A. Fernandez, for guidance and support. The researchers also appreciate the encouragement of their family and friends throughout the study.

Conflict of Interest Statement

The researchers and their research adviser declare that there are no conflicts of interest related to this study. The research was conducted solely for academic purposes, and no financial, personal, or professional relationships influenced the design, data collection, analysis, or interpretation of the findings.

References

1. Guillermo J (2024) Self-regulation and sports preparedness among collegiate athletes in Region III. Philippine Journal of Educational Research 18(2): 33-47.
2. Buenaventura M (2024) Self-efficacy and sports performance among secondary student-athletes in Quezon City. Philippine Journal of Sports Science 15(1): 44-56.
3. Aydan H (2025) Goal setting and motivation in self-regulated learning. Journal of Educational Psychology 117(2): 245-258.
4. Wolters C (2025) Time management and academic success in higher education. Contemporary Educational Psychology 72: 102101.
5. Sabaliauskas S, Kairys A, Zydziunaite V (2025) Metacognitive regulation and learning outcomes in university students. Frontiers in Psychology 16: 1345.
6. Liao Y, Huang T, Chen S (2022) Academic engagement among collegiate athletes: Balancing sports and academic responsibilities. Journal of College Student Development 63(4): 453-468.
7. Ge X, Ifenthaler D, Spector J (2025) Goal-oriented learning and academic engagement in higher education. Educational Technology Research and Development 73(1): 89-105.
8. Lourenço A (2024) Self-regulated learning strategies in higher education students. Educational Psychology Review 36(1): 1-22.
9. Lamban J, Reyes P, Cruz D (2025) Time-management practices among university student-athletes. Journal of Sports Education 12(3): 210-224.
10. Bao L, Chen Y, Huang R (2024) Metacognitive awareness and academic performance among student-athletes. International Journal of Educational Research 120: 102193.
11. Hey R, Weiss J, Schmidt M (2024) Metacognitive strategies and effort regulation in higher education. Learning and Individual Differences 98: 102178.
12. Zimmerman BJ (2002) Becoming a self-regulated learner: An overview. Theory Into Practice 41(2): 64-70.