Analysis of Artificial Intelligence (AI) Utilization for Improving Motor Skills Learning Outcomes among Elementary School Teacher Education (PGSD) Students
DOI:
https://doi.org/10.54259/diajar.v3i2.2492Kata Kunci:
Artificial Intelligence Second, Motor Skills , Elementary SchoolAbstrak
This study explores the use of artificial intelligence (AI) to improve motor skills learning outcomes for elementary school teacher education (PGSD) students. The research investigates how AI can provide timely feedback and personalized instruction in teaching motor skills. Data analysis methods include surveys, observations, and evaluations of learning outcomes. The research findings reveal the following: 1) Female students' motor skills are classified as moderate, with an average score of 200. The classification criteria for female students' motor skills are excellent (5.67%), good (24.33%), moderate (30.33%), poor (32.67%), and very poor (3%). 2) Male students' motor skills are also categorized as moderate, with an average score of 200. The percentage breakdown for male students' motor skills is as follows: excellent (7.32%), good (25.61%), moderate (37.80%), poor (29.39%), and very poor (4.88%). The results indicate that integrating AI into motor skills learning significantly enhances the academic performance of PGSD students. These findings highlight the importance of incorporating AI technology in teacher education, particularly to enhance the motor skills development of PGSD students. The research supports the concept that AI can effectively support motor skills learning at this educational level.
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Referensi
M. Martínez-Comesaña, X. Rigueira-Díaz, A. Larrañaga-Janeiro, J. Martínez-Torres, I. Ocarranza-Prado, and D. Kreibel, “Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review,” Rev. Psicodidact., 2023, doi: 10.1016/j.psicod.2023.06.001.
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M. Sampson et al., “Health Promotion Advocate Extends Benefits Offered to Adolescents, Young Adults.,” Addiction, 2016.
M. Martínez-Comesaña, X. Rigueira-Díaz, A. Larrañaga-Janeiro, J. Martínez-Torres, I. Ocarranza-Prado, and D. Kreibel, “Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review,” Rev. Psicodidact., 2023, doi: 10.1016/j.psicod.2023.06.001.
H. Dewitz, B. Yildirim, and P. Klein, “Biomechanical screening for injury prevention: The importance of 3D-motion analysis in high performance sports,” Unfallchirurg. 2018. doi: 10.1007/s00113-018-0498-3.
D. Lloyd, “The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries,” Sports Biomechanics. 2021. doi: 10.1080/14763141.2021.1959947.
T. De Jong et al., “INTERNATIONAL JOURNAL OF SPECIAL EDUCATION Vol 31, No: 1, 2016 EFFECTIVE PRACTICE IN INCLUSIVE AND SPECIAL NEEDS EDUCATION Jorun Buli-Holmberg,” Comput. Educ., 2015.
A. Harrington et al., “News and notes,” Procedia - Soc. Behav. Sci., 2015.
V. V. César Augusto, “La Curva Hibrida De Riesgo: Análisis Retrospectivo Y Prospectivo Del Riesgo Por Fenómenos Naturales,” Tesis, 2015.
J. Domaradzki and D. Koźlenia, “The performance of body mass component indices in detecting risk of musculoskeletal injuries in physically active young men and women,” PeerJ, 2022, doi: 10.7717/peerj.12745.
M. Sampson et al., “Health Promotion Advocate Extends Benefits Offered to Adolescents, Young Adults.,” Addiction, 2016.
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