International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol 16 No 17 (2022) Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming Learning Outcomes in Sports Education https://doi.org/10.3991/ijim.v16i17.34365 Syahrastani(), Hendra Hidayat, Anton Komaini, Andri Gemaini, Zulbahri Universitas Negeri Padang, Padang, Indonesia syahrastani@fik.unp.ac.id Abstract—Smart Application is one that can be used to learn swim- ming for students in sports education. This study aims to reveal and ex- plain the usability of smart swimming applications to explore factors in- fluencing students' swimming learning outcomes in sports education. Data of this study were 300 sports education students that took swim- ming courses. Statistical analysis was performed using multiple regres- sion analysis with the help of the software Statistical Package for the Social Sciences (SPSS) version 16. Overall, the factors of learning mo- tivation, physical activity, nutritional status, and V02Max have an F- table value of 105.605 > 2.25, while R2 is valued at 58.9%. The results of the t-test revealed that all those factors affecting swimming learning outcomes, with the t-count value are more significant than the t-table at a significance level less than 0.05, which is 0.000. Furthermore, all fac- tors are interrelated and needed to each other to produce good quality student swimming learning outcomes. Therefore, adequate attention and good management are necessary for lecturers in teaching swimming materials to improve students learning quality in sports education. Keywords—smart application, learning outcomes, learning motivation, physical activity, nutritional status, V02Max 1 Introduction Smart applications are becoming a trend in the development of information tech- nology and education [1], [2], including in the field of sports [3], [4]. Smart learning and mobile learning is in great demand in today's digital learning era, not least in swimming lessons in sports education [5]. Smart Application in swimming is an alter- native in helping to improve student learning outcomes [6], in addition, through the smart application in swimming one can explore the factors that influence student swimming learning outcomes in sports education. A swimmer's achievement is the result of training that can be measured and predicted accurately. Achieving this goal requires a learning process and motivation that stimulates good behaviour or moves. 116 http://www.i-jim.org https://doi.org/10.3991/ijim.v16i17.34365 mailto:syahrastani@fik.unp.ac.id Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Motivation and learning outcomes can influence to each other [7]. Learning motiva- tion is an impulse that occurs within individuals which always encourage them to improve certain qualities [8]. The learning outcome of a swimming lesson is not only determined by motivation and regular exercise [9], but also by excellent body condi- tion, good nutritional status, healthy physical activity, and maximum oxygen volume (VO2Max) of the athletes [10]. Physical factors and swimming techniques can be modified or improved through special exercises [11]. However, all of these factors should be accompanied with a good nutritional status. Lack of physical fitness and unbalanced nutritional status can interfere with a swimmer's development and growth [12]. Moreover, body movement when swim- ming requires energy [13], which comes from foods that contain sufficient nutrients [14]. Another factor affecting swimming learning outcomes is a physical activity directly related to specific body posture [15]. Physical activity includes all exercises, body movements, work, recreation, daily activities, and activities during vacation or leisure [16]. Swimming is strongly influenced by the ability to inhale a significant amount of oxygen called VO2Max [17], measured in milliliters in one minute per kilogram of the body [18]. VO2Max is used to measure the capacity of the heart, lungs, and blood to transport oxygen to various parts of the muscles, measured during exercise [19], that someone with a higher VO2Max value could train more intensively than those in harmful conditions [20]. Moreover, they can also perform more vigorous activities [21], dependent on cardiovascular, respiratory, hematology, and exercise ability [22]. Learning motivation, physical activities, nutritional status, and maximum oxygen volume (V02Max) that affect student swimming learning outcomes can run optimally by using the smart application in swimming. The use of smart applications in learning to swim is something that can provide students' interest and interest in learning to swim [23]. The benefits of using mobile learning in learning are to facilitate the teach- ing and learning process carried out in the classroom or outside the classroom [24], can attract students' attention and can also foster enthusiasm and can motivate stu- dents in learning so that the material being delivered can be easily understood. Swimming learning requires a clear understanding of practice and some students find it difficult to understand, the use of mobile learning can help understand the materials learning that students can learn and follow anywhere and can be repeated [25]. So this study tries to discuss and explain the usability of smart swimming applications to explore factors influencing students' swimming learning outcomes in sports educa- tion. 2 Research methods 2.1 Participants The research is a descriptive quantitative study. This study involved 300 partici- pants or students (Men: 227, Women: 73) of Universitas Negeri Padang that took part in training on swimming learning outcomes from October to December 2021. The iJIM ‒ Vol. 16, No. 17, 2022 117 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… participants have an age range of 18–24 years, a weight of 45–85 kg, a height of 150– 175 cm, a sitting height of 80–90 cm, and a leg length of 70–85 cm. They were in- formed about the aims and procedures of the research and expressed their willingness to participate in this study prior to the data collection. 2.2 Measures The data collection on swimming ability was carried out in 2 congregations. The first was an initial test to determine the students' swimming ability, and then the sec- ond data was taken after being given treatment. The data were measured based on the ability to swim freestyle for 50 meters. In measuring the ability of the freestyle swimming technique, the assessment score was obtained by referring to the observa- tion sheet, which was assessed including head, feet, hands, breathing and coordination of movements. The learning motivation and physical activity instruments were ob- tained using grids and questionnaire sheets. A questionnaire is a data collection tech- nique consisting of questions submitted to respondents [26]. Meanwhile, that it is a method of collecting data in written questions from respondents [27]. Furthermore, the instrument for the swimming learning outcomes is used in the form of an objective assessment. The instrument is arranged based on the following indicators: (1) the desire to succeed, (2) the encouragement and need for learning, (3) the hopes and aspirations for the future, (4) the appreciation for learning, (5) exciting activities, and (6) the conducive learning environment. These indicators were also used in the study carried out by Shepard et al [28]. As for swimming assessment, physical activity is done by recording the time score of swimming with a freestyle for 50 meters in length. Data collection was carried out in 2 congregations: pre-test and post-test. The scoring was obtained by dividing the time of the 50-meter freestyle swimming speed test, which was carried out twice. After the initial data collection, participants who became the research object at the next 16 meetings were given swimming training or treatment using the breaststroke and freestyle. This treatment was carried out with variations in the time and intensity of exercise in each meeting. Nutritional status is determined through anthropometric examination with Body Mass Index (BMI) by measuring weight in kilograms (Kg) and measuring height (TB) in meters (m) with the following formula [29]: BMI = The assessment used a descriptive percentage analysis technique to determine the overview of nutritional status based on the calculation of the weight and height index as shown in Table 1. 118 http://www.i-jim.org Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Table 1. BMI threshold category Category IMT Thin Severe weight loss < 17,0 Mild weight loss 17,0 - 18,5 Normal Ideal weight > 18,5 - 25,0 Fat Mild overweight > 25,0 - 27,0 Excess weight level > 27,0 Source: [29] 2.3 Procedures Prior to the experiment, participants were informed of the purpose and procedures of the study and expressed willingness to participate. Then students are asked to ac- cess the smart application in swimming to understand the swimming content in smart application in swimming and at the same time be able to use it when swimming les- sons are being implemented. This application is known as SwimUp-Swimming Train- ing, which is a free smart swimming application available on the Playstore, as shown in Figures 1 and 2. Fig. 1. Swimming training planning features Fig. 2. Swimming theory features iJIM ‒ Vol. 16, No. 17, 2022 119 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Furthermore, participants then completed the learning motivation questionnaires. Nutritional status was gathered by collecting physical data of the participants, includ- ing age, height, and weight. The VO2Max of each participant was measured using Multi-Stage Fitness Test, made up of 23 stages where each stage lasts for about one minute. Each stage comprises a series of 20 meters shuttles where the starting speed is 8.5 km/hour and increases by 0.5km/hour at each stage. A single beep indicates the end of a shuttle, and three beeps indicate the start of the next stage. Swimming lessons were conducted in 16 sessions. In each session, participants were trained with the 50-meter freestyle swimming technique. Prior to the lessons, each participant was tested for their swimming technique skills. Then learning out- comes were measured by testing the participants' swimming technique skills after swimming lessons were completed. 2.4 Statistical analysis A Multiple Linear Regression Analysis was used to determine the factors that influence swimming learning outcomes. This analysis is in line with the study carried out [30], which states that multiple regression analysis is used to predict the rise and fall of 2 or more independent factors (learning motivation, physical activity, nutritional status, and VO2Max) on the dependent (swimming learning outcomes). This analysis was carried out on more than 2 factors, with the following equation: Y = a + b1X1 + b2X2+ b3X3+ b4X4. E Information: Y = Swimming Learning Outcomes a = Constant, b = Multiple Regression Coefficient X1 = Learning Motivation X2 = Physical Activity X3 = Nutritional status X₄ = Maximum Oxygen Volume (V02Max) b1, b2, b3, ......bn = regression coefficient. e = error Before estimating the multiple linear regression models, the data used were free- deviations from the classical assumptions, which are multicollinearity and heterosce- dasticity tests. This was preceded by the Ftest, the Coefficient of Determination Test (R2), and the ttest. Meanwhile, statistical analysis was performed using SPSS (Statistical Package for the Social Sciences) version 16. 2.5 Ethical considerations This research has been carried out in accordance with 7 (seven) WHO 2011 Stand- ards, namely 1) Social Values, 2) Scientific Values, 3) Equal distribution of Burdens and Benefits, 4) Risks, 5) Persuasions/Exploitation, 6) Confidentiality and Privacy, and 7) Approval after explanation, which refers to the 2016 CIOMS Guidelines. This 120 http://www.i-jim.org Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… research has also been approved by the Health Research Ethics Committee of Univer- sitas Negeri Padang (No.18.01/KEPK-UNP/II/2021). 3 Results and discussion Before the data analysis process is carried out, it is necessary to test multicollinearity and heteroscedasticity, the results are as follows. 3.1 Multicollinearity test Multicollinearity test is a test carried out to determine whether in a regression model there is an inter-correlation or collinearity amongst independent variables. Inter-correlation is a linear relationship or a strong relationship between one inde- pendent variable or predictor variable with other predictor variables in a regression model. Table 2 shows the multicollinearity test results with the lowest and highest tolerance values of 0.890 and 0.983 in the V02Max Learning Motivation factors above 0.1. Meanwhile, the lowest and highest VIF values of 1.017 and 1.123 were found in the Learning Motivation and the V02Max factors, which is less than 10. Therefore, the model does not have symptoms of multicollinearity, which means that there is no correlation amongst independent factors. Table 2. Multicollinearity test results Model Collinearity Statistics Tolerance VIF 1 (Constant) Learning Motivation .983 1.017 Physical Activity .945 1.058 Nutritional status .946 1.057 V02Max .890 1.123 3.2 Multicollinearity test The scatterplot graph shows the respondent's points spread out without forming a certain pattern, like in Figure 3. Therefore, it can be concluded that the regression model lacked a heteroscedasticity problem. This means there is no similarity of resid- ual variance from one observation to another. iJIM ‒ Vol. 16, No. 17, 2022 121 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Fig. 3. Scatterplot graph Furthermore, after 16 meetings of swimming lessons, the results of the F-Test, Co- efficient of determination (R2), t-Test scores were obtained as follows: The F statistical test shows the joint effect of the independent factors included in the dependent model. The results of the Ftest are shown in Table 3. Table 3. F Test results ANOVA Model Sum of Squares Df Mean Square F Sig. Regression 19750.161 4 4937.540 105.605 .000a Residual 13792.683 295 46.755 Total 33542.844 299 a. Predictors: (Constant), V02Max, Learning Motivation, Nutritional Status, Physical Activity b. Dependent Variable: Swimming learning outcomes Table 3 shows that the numerator and denominator values are 4 and 295, with an F- table of 2.4022. The calculated F-value is greater than the F-table, which is 105.605 > 2.25. The significance level also shows 0.000, which is smaller than the 5% signifi- cance level (α) of 0.05, therefore it can be concluded that the independent factors simultaneously affect the dependent. Overall ,the 4 factors are learning motivation, physical activity, nutritional status, and V02Max on the dependent factor (swimming learning outcomes). The value of the determination coefficient in the regression results is shown in Ta- ble 4, with an adjusted R-Square value of 0.589. This indicates that 58.9% of swim- ming learning outcomes factors are explained by Learning Motivation, Nutritional Status, Physical Activity, and V02Max. Meanwhile, 42.1% is influenced by other factors not included in the regression model. 122 http://www.i-jim.org Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… Table 4. Coefficient of determination Model R R Square Adjusted R Square Std. An error of the Estimate 1 .767a .589 .583 6.83775 a. Predictors: (Constant), Learning Motivation, Nutritional Status, Physical Activity, V02Max b. Dependent Variable: Swimming Learning Outcomes Statistical ttest is basically used to show the influence of each independent factor in explaining the variation of the dependent factor. The percentage point of the distribu- tion of t (df= 0.05: 394) is 1.966. The results of the calculations are shown in Table 5. Table 5. t-test calculation results Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta (Constant) -19.657 3.561 -5.520 .000 Learning Motivation .541 .042 .484 12.859 .000 Physical Activity .350 .037 .369 9.594 .000 Nutritional status .189 .044 .166 4.317 .000 V02Max .297 .046 .257 6.501 .000 a. Dependent Variable: Swimming Learning Outcomes The regression equation is formed as follows: Y = -19.657+ 0,541X1 + 0,350X2 + 0,189X3 + 0,297X4 Regression coefficients on learning motivation, physical activity, nutritional status, and V02Max of the dependent factors have a positive effect. Furthermore, the t-count value of each factor also shows that the t-count is greater than the t-table > 1.966, and the significance level is 0.000, which is smaller than 0.05. The effects of each factor are interpreted in Table 6. Table 6. Each factor effects No Factor t count t table Sig Description 1 Learning Motivation 12.859 1,966 .000 Effect & Significant 2 Physical Activity 9.594 1,966 .000 Effect & Significant 3 Nutritional status 4.317 1,966 .000 Effect & Significant 4 V02Max 6.501 1,966 .000 Effect & Significant The use of smart applications in various purposes is a current trend, the ease and effectiveness of this application is the main purpose of its use. Research on the use of smart applications can be found in various purposes such as youth financial manage- ment [31], science learning in primary school [32], disseminating messages and events of university colleges [33], and the formation of analytical competence of iJIM ‒ Vol. 16, No. 17, 2022 123 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… future specialists [34], including in the field of sports education [35]. The use of smart applications in sports education, such as the smart application in swimming, is inter- esting to discuss, because the use of smart applications in learning to swim is some- thing that can give students interest and interest in learning to swim. So that it affects students' swimming learning outcomes and is able to explore factors influencing stu- dents' swimming learning outcomes in sports education. Students' swimming learning outcomes are influenced by factors such as learning motivation, physical activities, nutritional status, and maximum oxygen volume (V02Max). Learning motivation factors increases sports education students' swimming learn- ing outcomes, because this factor has a significant level lower than 0.05, which is 0.000. Everyone has a different motivation for an activity. With motivation, a person can determine the activities carried out according to his sense of self. Thus, it is as- sumed that self-knowledge, motivation, and self-control significantly affect exercise intensity and can prevent stimuli that interfere with the goals to be achieved [36]. When a person is intrinsically motivated to do something, they will engage in the activity because they are interested in and enjoy it. Further, physical activity factors significantly affect swimming learning outcomes because the better a swimmer's body condition, the greater his or her ability, this factor has a significant level lower than 0.05, which is 0.000. An increase in activity leads to a rise in the body's work meta- bolic system, which stimulates the need for food intake. This is influenced by the level of energy needed and the metabolic used based on the activity and intensity of each teenager. More intense activity stimulates the body's metabolic system into ener- gy and vice versa [37]. Moreover, an exercise program that involve much physical activity for 33 weeks significantly improve students' health and skills in swimming learning [38]. Therefore, it can be concluded that physical activity stimulates the me- tabolism of a swimmer's body into energy which affects the learning outcomes of sports education students. The nutritional status factor significantly affects the swimming learning outcomes because a swimmer is more influential at a learning outcome, this factor has a signifi- cance level lower than 0.05, which is 0.000. Moreover, nutritional status in adoles- cents can also be influenced by direct and indirect factors, such as adequate nutrition- al intake, patterns of physical activity, food, sanitation, and economic influences. It is essential to focus on the nutritional intake of adolescents because when the amount is not intense, it tends to have a significant impact on swimmers' ability to carry out this activity. Moreover, lack of nutritional intake also affects adolescents' immunity, which is more susceptible to disease [39]. Nutritional factors may be indirectly involved with some aspects of acute injury. Inadequate nutrition contributes to the fatigue that facili- tates poor concentration and technique or it can increase risk of accidents [40]. Good physical health will significantly support a person in carrying out daily tasks without causing significant fatigue. However, physical fitness is not entirely influenced by physical activity and nutritional intake but it is also influenced by healthy living be- haviours. Thus, it can be stated that physical activity and nutritional status can affect physical fitness [41]. The sample in this study comprises students in sports education. They are categorized as teenagers that are independent in choosing their nutritional 124 http://www.i-jim.org Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… intake. Therefore, when the nutritional intake does not fulfill the body's needs, it af- fects negatively the ability to swim. Furthermore, V02Max factor significantly affects swimmers, leading to a more ef- fective learning outcome, this factor has a significance level lower than 0.05, which is 0.000. Therefore, based on the findings, the calculated t-value of the V02Max factor ranks third after learning motivation and physical activity, which indicates an im- portant attribute. Their study is conducted to identify the influential factors in the exercise program to improve athletes' performance. All factors that affect student swimming learning outcomes can be assisted by the smart application in swimming, because of the nature of this smart application as a student self tutor in mastering swimming skills, so that it has an impact on student learning outcomes. The findings reveal that learning motivation, physical activity, nutritional status, and V02Max are interrelated and influential factors that are used to succeed in swimming learning outcomes for sports education students. In addition, optimal swimming practice im- pacts good swimming learning outcomes [42], [43], and supported by the use of swimming application technology [44], [45], and other technology needs [46], [47]. 4 Conclusion The results of this study reveal that smart applications are very important in sup- porting learning, including learning in sports education in swimming courses. The smart application in swimming helps students to be independent and become more interested in swimming courses. So that it indirectly has an impact on students' swimming learning outcomes, and it was also revealed that learning motivation, phys- ical activity, nutritional status, and maximum oxygen volume (V02Max) have a sig- nificant effect on increasing sports education students' swimming learning outcomes. It is found that learning motivation can improve the swimming learning outcomes of sports education students. This variable provides more significant contribution com- pared to others. Each swimmer has a different learning motivation in swimming activ- ities, this motivation factor is also interrelated with other related variables. The physi- cal activity, nutritional status, and V02Max factor also significantly influence the swimming learning outcomes of sports education students. The research findings indicate that students’ learning outcomes can be increased by integrating learning motivation, physical activity, nutritional status, and maximum oxygen volume varia- bles. The factors that are tested and interact with each other can be justified in im- proving the quality of students’ learning outcomes. 5 Acknowledgment Thank you to all who have facilitated and contributed to conducting research, the lecturers, and staff at the Faculty and Department, the experts who contributed in providing advice during the implementation of the research. This research activity was also assisted by various other teams to provide comments and information about iJIM ‒ Vol. 16, No. 17, 2022 125 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… the teaching and learning in sports education. Furthermore, thanks to the staff and operators who facilitated the work. 6 References [1] A. Szymkowiak, B. Melović, M. Dabić, K. Jeganathan, and G. S. Kundi, “Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people,” Technology in Society, vol. 65, p. 101565, May 2021. https://doi.org/ 10.1016/j.techsoc.2021.101565 [2] A. A. Aziz, M. U. Hassan, H. Dzakiria, and Q. Mahmood, “Growing Trends of Using Mo- bile in English Language Learning,” Mediterranean Journal of Social Sciences, vol. 9, no. 4, pp. 235–239, Jul. 2018. https://doi.org/10.2478/mjss-2018-0132 [3] J. Lim, “Measuring sports performance with mobile applications during the COVID-19 pandemic,” p. 4, 2020. [4] E. Glebova and M. Desbordes, “Technology Enhanced Sports Spectators Customer Expe- riences: Measuring and Identifying Impact of Mobile Applications on Sports Spectators Customer Experiences,” AJSPO, vol. 7, no. 2, pp. 115–140, May 2020. https://doi.org/ 10.30958/ajspo.7-2-3 [5] X. Zhu and F. Kou, “Three-dimensional simulation of swimming training based on An- droid mobile system and virtual reality technology,” Microprocessors and Microsystems, vol. 82, p. 103908, Apr. 2021. https://doi.org/10.1016/j.micpro.2021.103908 [6] H.-C. Chu, “Potential Negative Effects of Mobile Learning on Students’ Learning Achievement and Cognitive Load—A Format Assessment Perspective,” 2014, vol. 17, no. 1, pp. 332–344, [Online]. Available: https://www.jstor.org/stable/jeductechsoci.17.1.332 [7] A. Buchori, P. Setyosari, I. W. Dasna, S. Ulfa, I. N. S. Degeng, and C. Sa’dijah, “Effec- tiveness of Direct Instruction Learning Strategy Assisted by Mobile Augmented Reality and Achievement Motivation on Students Cognitive Learning Results,” ASS, vol. 13, no. 9, p. 137, Aug. 2017. https://doi.org/10.5539/ass.v13n9p137 [8] R. H. Rafiola, P. Setyosari, C. L. Radjah, and M. Ramli, “The Effect of Learning Motiva- tion, Self-Efficacy, and Blended Learning on Students’ Achievement in The Industrial Revolution 4.0,” Int. J. Emerg. Technol. Learn., vol. 15, no. 08, p. 71, Apr. 2020. https://doi.org/10.3991/ijet.v15i08.12525 [9] L. Meroño, A. Calderón, and P. A. Hastie, “Effect of Sport Education on the technical learning and motivational climate of junior high performance swimmers,” Rev. int. cienc. deporte. https://doi.org/10.5232/ricyde [10] M. Kuswari, R. Nuzrina, N. Gifari, P. Dhyani Swamilaksita, and J. Tri Hapsari, “Correla- tion Intake of Energy, Protein, Fluid, Physical Activity, and Hydration Status with VO2Max at Hockey Atlet in UKM Pancasila University:,” in Proceedings of the 1st Inter- national Conference on Recent Innovations, Jakarta, Indonesia, 2018, pp. 512–518. https://doi.org/10.5220/0009951805120518 [11] Q.-H. Vuong, A.-D. Hoang, T.-T. Vuong, V.-P. La, H. Nguyen, and M.-T. Ho, “Factors Associated with the Regularity of Physical Exercises as a Means of Improving the Public Health System in Vietnam,” Sustainability, vol. 10, no. 11, p. 3828, Oct. 2018. https://doi. org/10.3390/su10113828 [12] A. Mizugaki, H. Kato, H. Suzuki, H. Kurihara, and F. Ogita, “Nutritional Practice and Ni- trogen Balance in Elite Japanese Swimmers during a Training Camp,” Sports, vol. 9, no. 2, p. 17, Jan. 2021. https://doi.org/10.3390/sports9020017 126 http://www.i-jim.org https://doi.org/10.1016/j.techsoc.2021.101565 https://doi.org/10.1016/j.techsoc.2021.101565 https://doi.org/10.2478/mjss-2018-0132 https://doi.org/10.30958/ajspo.7-2-3 https://doi.org/10.30958/ajspo.7-2-3 https://doi.org/10.1016/j.micpro.2021.103908 https://www.jstor.org/stable/jeductechsoci.17.1.332 https://doi.org/10.5539/ass.v13n9p137 https://doi.org/10.3991/ijet.v15i08.12525 https://doi.org/10.5232/ricyde https://doi.org/10.5220/0009951805120518 https://doi.org/10.3390/su10113828 https://doi.org/10.3390/su10113828 https://doi.org/10.3390/sports9020017 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… [13] B. J. Gemmell, S. P. Colin, and J. H. Costello, “Widespread utilization of passive energy recapture in swimming medusae,” Journal of Experimental Biology, p. jeb.168575, Jan. 2017. https://doi.org/10.1242/jeb.168575 [14] S. Robertson and M. Mountjoy, ““A Review of Prevention, Diagnosis and Treatment of Relative Energy Deficiency in Sport (RED-S) in Artistic (Synchronized) Swimming,” Wil- derness & Environmental Medicine, vol. 24, no. 1, pp. 78–79, Mar. 2013. https://doi.org/ 10.1016/j.wem.2012.11.017 [15] I. M. Lahart and G. S. Metsios, “Chronic Physiological Effects of Swim Training Interven- tions in Non-Elite Swimmers: A Systematic Review and Meta-Analysis,” Sports Med, vol. 48, no. 2, pp. 337–359, Feb. 2018. https://doi.org/10.1007/s40279-017-0805-0 [16] K. Hallmann and T. Giel, “eSports – Competitive sports or recreational activity?,” Sport Management Review, vol. 21, no. 1, pp. 14–20, Jan. 2018. https://doi.org/10.1016/j.smr. 2017.07.011 [17] T. Trisaptono and S. Sumintarsih, “Effect of Circuit Training, Interval Training and Body Mass Index For Increase the VO2 Max,” in Proceeding of LPPM UPN “VETERAN” Yog- yakarta Conference Series 2020- Political and Social Science Series, Oct. 2020, pp. 23– 31. https://doi.org/10.31098/pss.v1i1.86 [18] I. A. Budiman, “Comparison of the effects of fartlek exercise and interval training towards the improvement of Vo2 maximum,” Int. j. phys. educ. sports health. [19] G. Berthelot et al., “Technology & swimming: 3 steps beyond physiology,” Materials To- day, vol. 13, no. 11, pp. 46–51, Nov. 2010. https://doi.org/10.1016/S1369-7021(10)70203- 0 [20] C. Gjestvang, T. Stensrud, and L. A. H. Haakstad, “How is rating of perceived capacity re- lated to VO 2max and what is VO 2max at onset of training?,” BMJ Open Sport Exerc Med, vol. 3, no. 1, p. e000232, Jul. 2017. https://doi.org/10.1136/bmjsem-2017-000232 [21] Ø. Støren et al., “The Effect of Age on the V˙O2max Response to High-Intensity Interval Training,” Medicine & Science in Sports & Exercise, vol. 49, no. 1, pp. 78–85, Jan. 2017. https://doi.org/10.1249/MSS.0000000000001070 [22] C. Williams, “Sports nutrition (handbook of sports medicine and science),” British Journal of Sports Medicine, vol. 39, no. 5, pp. 307–307, May 2005. https://doi.org/10.1136/bjsm. 2004.008953 [23] A. Ahn, “Swim This Way Mobile App: Instructional Design for Improved Technical Skill for Freestyle Swimming,” 2022, vol. LTEC 690, Spring 2022, pp. 1–55, [Online]. Availa- ble: http://hdl.handle.net/10125/81822 [24] M. Sarrab, “Mobile Learning (M-Learning) and Educational Environments,” IJDPS, vol. 3, no. 4, pp. 31–38, Jul. 2012. https://doi.org/10.5121/ijdps.2012.3404 [25] L. Dias and A. Victor, “Teaching and Learning with Mobile Devices in the 21st Century Digital World: Benefits and Challenges,” Europ. j. multidiscip. stud., vol. 5, no. 1, p. 339, May 2017. https://doi.org/10.26417/ejms.v5i1.p339-344 [26] P. Lietz, “Research into Questionnaire Design: A Summary of the Literature,” Interna- tional Journal of Market Research, vol. 52, no. 2, pp. 249–272, Mar. 2010. https://doi.org/ 10.2501/S147078530920120X [27] M. Patten, Questionnaire Research A Practical Guide, 4th ed. New York: Routledge. [Online]. Available: https://doi.org/10.4324/9781315265858 [28] L. A. Shepard, W. R. Penuel, and J. W. Pellegrino, “Using Learning and Motivation Theo- ries to Coherently Link Formative Assessment, Grading Practices, and Large-Scale As- sessment,” Educational Measurement: Issues and Practice, vol. 37, no. 1, pp. 21–34, Mar. 2018. https://doi.org/10.1111/emip.12189 iJIM ‒ Vol. 16, No. 17, 2022 127 https://doi.org/10.1242/jeb.168575 https://doi.org/10.1016/j.wem.2012.11.017 https://doi.org/10.1016/j.wem.2012.11.017 https://doi.org/10.1007/s40279-017-0805-0 https://doi.org/10.1016/j.smr.2017.07.011 https://doi.org/10.1016/j.smr.2017.07.011 https://doi.org/10.31098/pss.v1i1.86 https://doi.org/10.1016/S1369-7021(10)70203-0 https://doi.org/10.1016/S1369-7021(10)70203-0 https://doi.org/10.1136/bmjsem-2017-000232 https://doi.org/10.1249/MSS.0000000000001070 https://doi.org/10.1136/bjsm.2004.008953 https://doi.org/10.1136/bjsm.2004.008953 http://hdl.handle.net/10125/81822 https://doi.org/10.5121/ijdps.2012.3404 https://doi.org/10.26417/ejms.v5i1.p339-344 https://doi.org/10.2501/S147078530920120X https://doi.org/10.2501/S147078530920120X https://doi.org/10.4324/9781315265858 https://doi.org/10.1111/emip.12189 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… [29] W. Guan, A. Thaw, S. N. Grondhuis, and A. Schaechter, “Evaluation of a Commercial Telehealth Weight Loss and Management Program,” Adv Weigh Loss Manag Med Dev, vol. 03, no. 02, 2018. https://doi.org/10.35248/2593-9793.18.3.114 [30] T. Z. Keith, Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling, 3rd ed. Third Edition. | New York : Routledge, 2019. | Re- vised edition of the author’s Multiple regression and beyond, 2015.: Routledge, 2019. https://doi.org/10.4324/9781315162348 [31] A. Mueangpud, J. Khlaisang, and P. Koraneekij, “Mobile Learning Application Design to Promote Youth Financial Management Competency in Thailand,” Int. J. Interact. Mob. Technol., vol. 13, no. 12, p. 19, Dec. 2019. https://doi.org/10.3991/ijim.v13i12.11367 [32] M. A/P Tachinamutu, M. N. H. Bin Mohamad Said, Z. Binti Abdullah, M. F. Bin Ali, and L. Bin Mohd Tahir, “The Effect of Using ‘Microb’ Mobile Application in Science Learn- ing in Primary School,” Int. J. Interact. Mob. Technol., vol. 16, no. 02, pp. 82–100, Jan. 2022. https://doi.org/10.3991/ijim.v16i02.27307 [33] G. M. ALFarsi, J. Jabbar, and M. ALSinani, “Implementing a Mobile Application News Tool for Disseminating Messages and Events of AlBuraimi University College,” Int. J. In- teract. Mob. Technol., vol. 12, no. 7, p. 129, Nov. 2018. https://doi.org/10.3991/ijim.v12i7. 9484 [34] O. V. Galustyan, A. P. Smetannikov, I. G. Kolbaya, G. S. Palchikova, D. V. Galigorov, and O. B. Mazkina, “Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists,” Int. J. Interact. Mob. Technol., vol. 14, no. 02, p. 242, Feb. 2020. https://doi.org/10.3991/ijim.v14i02.11658 [35] G. Rospo et al., “Cardiorespiratory Improvements Achieved by American College of Sports Medicine’s Exercise Prescription Implemented on a Mobile App,” JMIR Mhealth Uhealth, vol. 4, no. 2, p. e77, Jun. 2016. https://doi.org/10.2196/mhealth.5518 [36] R. M. Ryan and E. L. Deci, “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being,” American Psychologist, p. 11, 2000. https://doi.org/10.1037/0003-066X.55.1.68 [37] G. A. Hand et al., “The Effect of Exercise Training on Total Daily Energy Expenditure and Body Composition in Weight-Stable Adults: A Randomized, Controlled Trial,” Jour- nal of Physical Activity and Health, vol. 17, no. 4, pp. 456–463, Apr. 2020. https://doi.org/ 10.1123/jpah.2019-0415 [38] A. Naczk, E. Gajewska, and M. Naczk, “Effectiveness of Swimming Program in Adoles- cents with Down Syndrome,” IJERPH, vol. 18, no. 14, p. 7441, Jul. 2021. https://doi.org/ 10.3390/ijerph18147441 [39] P. Bergman and S. Brighenti, “Targeted Nutrition in Chronic Disease,” Nutrients, vol. 12, no. 6, p. 1682, Jun. 2020. https://doi.org/10.3390/nu12061682 [40] L. M. Burke and M. M. Manore, “Nutrition for sport and physical activity,” in Present Knowledge in Nutrition, Elsevier, 2020, pp. 101–120. https://doi.org/10.1016/B978-0-12- 818460-8.00006-X [41] J. J. Otten, J. P. Hellwig, and L. D. Meyers, DRI, dietary reference intakes: the essential guide to nutrient requirements. Washington, D.C.: National Academies Press, 2006. Ac- cessed: Jul. 27, 2022. [Online]. Available: http://public.ebookcentral.proquest.com/ choice/publicfullrecord.aspx?p=3378838 [42] S. Ivanenko et al., “Analysis of the indicators of athletes at leading sports schools in swimming,” p. 6. [43] K. Görner, L. Kručanica, and Z. Sawicki, “Selected socio-economic factors influencing swimming competency of secondary school students,” p. 7. 128 http://www.i-jim.org https://doi.org/10.35248/2593-9793.18.3.114 https://doi.org/10.4324/9781315162348 https://doi.org/10.3991/ijim.v13i12.11367 https://doi.org/10.3991/ijim.v16i02.27307 https://doi.org/10.3991/ijim.v12i7.9484 https://doi.org/10.3991/ijim.v12i7.9484 https://doi.org/10.3991/ijim.v14i02.11658 https://doi.org/10.2196/mhealth.5518 https://doi.org/10.1037/0003-066X.55.1.68 https://doi.org/10.1123/jpah.2019-0415 https://doi.org/10.1123/jpah.2019-0415 https://doi.org/10.3390/ijerph18147441 https://doi.org/10.3390/ijerph18147441 https://doi.org/10.3390/nu12061682 https://doi.org/10.1016/B978-0-12-818460-8.00006-X https://doi.org/10.1016/B978-0-12-818460-8.00006-X http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=3378838 http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=3378838 Paper—Smart Application for Smart Learning: How the Influence of the Factors on Student Swimming… [44] P. Perego, G. Andreoni, R. Sironi, S. Lenzi, and G. Santambrogio, “Wearable device for swim assessment: a new ecologic approach for communication and analysis,” London, Great Britain, 2015. https://doi.org/10.4108/eai.14-10-2015.2261818 [45] A. Kos and A. Umek, “Reliable Communication Protocol for Coach Based Augmented Biofeedback Applications in Swimming,” Procedia Computer Science, vol. 174, pp. 351– 357, 2020. https://doi.org/10.1016/j.procs.2020.06.098 [46] E. Tasrif, H. K. Saputra, D. Kurniadi, H. Hidayat, and A. Mubai, “Designing Website- Based Scholarship Management Application for Teaching of Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) Subjects,” Int. J. Interact. Mob. Technol., vol. 15, no. 09, p. 179, May 2021. https://doi.org/10.3991/ijim.v15i09.23513 [47] A. Komaini et al., “Motor Learning Measuring Tools: A Design and Implementation Us- ing Sensor Technology for Preschool Education,” Int. J. Interact. Mob. Technol., vol. 15, no. 17, p. 177, Sep. 2021. https://doi.org/10.3991/ijim.v15i17.25321 7 Authors Syahrastani is a Doctor in Sports Education. He is a Senior Lecture in Department of Sports Education, Sports Science Faculty of Universitas Negeri Padang, Padang, Indonesia. His research areas include Pedagogy, and Sport Education. Hendra Hidayat, He is a Lecturer in in Department of Electronics Engineering Education, Engineering Faculty of Universitas Negeri Padang, Padang, Indonesia. His research areas include Technical Vocational and Education Training, and Engineering Education (Contact e-mail: hendra.hidayat@ft.unp.ac.id). Anton Komaini, He is a Lecture in Department of Sport Science, Sports Science Faculty of Universitas Negeri Padang, Padang, Indonesia. His research areas include Motor Learning, and Sport Science (Contact e-mail: antonkomaini@fik.unp.ac.id). Andri Gemaini, He is a Lecture in Department of Sport Science, Sports Science Faculty of Universitas Negeri Padang, Padang, Indonesia. His research areas include Sport Science, and Sport Tourism (Contact e-mail: andrigemaini@fik.unp.ac.id). Zulbahri, He is a Lecturer in Sports Education, Sports Science Faculty of Univer- sitas Negeri Padang, Padang, Indonesia. His research areas include Sports Education (Contact e-mail: zulbahri@fik.unp.ac.id). Article submitted 2022-07-05. Resubmitted 2022-08-10. Final acceptance 2022-08-11. Final version published as submitted by the authors. iJIM ‒ Vol. 16, No. 17, 2022 129 https://doi.org/10.4108/eai.14-10-2015.2261818 https://doi.org/10.1016/j.procs.2020.06.098 https://doi.org/10.3991/ijim.v15i09.23513 https://doi.org/10.3991/ijim.v15i17.25321