Innovation in Micro-Learning Content for Physical Education Teacher Training: Integrating Motion Capture Technology to Revolutionize IT-Based Instruction

Authors

DOI:

https://doi.org/10.53905/ChildDev.v2i01.02

Keywords:

micro-learning, motion capture, physical education teacher training, psychomotor learning, learner engagement

Abstract

Purpose of the study: Micro-learning has gained increasing attention as an effective instructional approach for delivering concise and focused learning content, particularly in teacher education. However, its application in Physical Education Teacher Training (PETT) remains limited, especially in addressing the integration of cognitive knowledge and psychomotor skill development. The integration of motion capture (MoCap) technology offers new opportunities to enhance embodied learning through real-time biomechanical feedback and interactive visualization. This study aimed to develop MoCap-integrated micro-learning content for PETT and to examine its effects on cognitive outcomes, psychomotor performance, learner satisfaction, and engagement.

Materials and methods: A quasi-experimental design was conducted with 80 PETT students (mean age = 23.5 years) from an Indonesian public university. Participants were randomly assigned to an experimental group (MoCap-based micro-learning) or a control group (traditional micro-learning). The intervention lasted eight weeks, with weekly sessions of 15–20 minutes. Cognitive knowledge, psychomotor performance, learner satisfaction, and engagement were assessed using validated instruments. Data were analyzed using independent and paired-samples t-tests and Pearson correlation analysis.

Results: The experimental group demonstrated significantly higher post-test scores in cognitive knowledge, psychomotor performance, learner satisfaction, and engagement compared to the control group (p < 0.001). Strong positive correlations were found between MoCap-based engagement and learning outcomes (r = 0.68–0.74). Learning gains were substantially greater in the experimental group across all measured domains.

Conclusions: MoCap-enhanced micro-learning significantly improves cognitive and psychomotor outcomes while increasing learner engagement and satisfaction in PETT. This approach effectively bridges the gap between theoretical understanding and practical skill execution, highlighting its potential as an innovative and scalable model for IT-based physical education teacher training.

References

Abrahamson, D., Tancredi, S., Chen, R. S. Y., Flood, V. J., & Dutton, E. (2023). Embodied Design of Digital Resources for Mathematics Education: Theory, Methodology, and Framework of a Pedagogical Research Program. In Springer international handbooks of education (p. 1). Springer Nature (Netherlands). https://doi.org/10.1007/978-3-030-95060-6_8-1

Alias, N. F., & Razak, R. A. (2023). Meeting the Demands of Higher Education: Examining Teaching and Learning Practices and Academic Challenges. Asian Journal of University Education, 19(4), 796. https://doi.org/10.24191/ajue.v19i4.24795

Bakk, Á. K., Bényei, J., Ballack, P., & Parente, F. (2025). Current possibilities and challenges of using metaverse-like environments and technologies in education. Frontiers in Virtual Reality, 6. https://doi.org/10.3389/frvir.2025.1521334

Castro-Alonso, J. C., Ayres, P., Zhang, S., Koning, B. B. de, & Paas, F. (2024). Research Avenues Supporting Embodied Cognition in Learning and Instruction. Educational Psychology Review, 36(1). https://doi.org/10.1007/s10648-024-09847-4

Chun, J., Kim, J.-S., Kim, H., Lee, G., Cho, S., Kim, C., Chung, Y. B., & Heo, S. (2025). A Comparative Analysis of On-Device AI-Driven, Self-Regulated Learning and Traditional Pedagogy in University Health Sciences Education. Applied Sciences, 15(4), 1815. https://doi.org/10.3390/app15041815

Diem, H. T. T., Thinh, M. P., & Lam, V. (2024). Exploring Practical Pedagogy in High School Biology Education: A Qualitative Study of Pre-Service Biology Teachers’ Experiences in Vietnam. European Journal of Educational Research, 557. https://doi.org/10.12973/eu-jer.13.2.557

Fidan, M. (2023). The effects of microlearning-supported flipped classroom on pre-service teachers’ learning performance, motivation and engagement. Education and Information Technologies, 28(10), 12687. https://doi.org/10.1007/s10639-023-11639-2

Fung, K. Y., Lee, L., Sin, K. F., Song, S., & Qu, H. (2024). Humanoid robot-empowered language learning based on self-determination theory. Education and Information Technologies, 29(14), 18927. https://doi.org/10.1007/s10639-024-12570-w

Garivaldis, F., McKenzie, S., Henriksen, D., & Studente, S. (2022). Achieving lasting education in the new digital learning world. Australasian Journal of Educational Technology, 38(4), 1. https://doi.org/10.14742/ajet.8331

Hazard, R., Eplin, R., Li, L., & Owusu‐Agyeman, Y. (2025). Action Research Teacher Training Within a Project-Based Learning Paradigm. International Journal of Learning Teaching and Educational Research, 24(8), 965. https://doi.org/10.26803/ijlter.24.8.43

Huang, Y., Backman, S. J., Backman, K. F., McGuire, F. A., & Moore, D. (2018). An investigation of motivation and experience in virtual learning environments: a self-determination theory. Education and Information Technologies, 24(1), 591. https://doi.org/10.1007/s10639-018-9784-5

Jainuri, M., Kamid, K., Syaiful, S., & Huda, N. (2025). Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes [Review of Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes]. Mathema Jurnal Pendidikan Matematika, 7(2), 630. https://doi.org/10.33365/jm.v7i2.517

Kiefer, M., & Trumpp, N. M. (2012). Embodiment theory and education: The foundations of cognition in perception and action. Trends in Neuroscience and Education, 1(1), 15. https://doi.org/10.1016/j.tine.2012.07.002

Kohnke, L., Foung, D., & Zou, D. (2023). Microlearning: A new normal for flexible teacher professional development in online and blended learning. Education and Information Technologies, 29(4), 4457. https://doi.org/10.1007/s10639-023-11964-6

Leysens, G., Claus, R., Petegem, W. V., & Charlier, N. (2025). Video annotation tools for assessing psychomotor skills in nursing education: A scoping review [Review of Video annotation tools for assessing psychomotor skills in nursing education: A scoping review]. International Journal of Assessment Tools in Education, 12(4), 1106. International Journal of Assessment Tools in Education. https://doi.org/10.21449/ijate.1713558

Li, K., Li, Q., Wang, L., Xiao-nan, W., Ding, X., & Liu, B. (2025). Virtual simulation experiments in medical education: technology acceptance, learning outcomes, and motivational impacts. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-08025-6

Li, Y., Wang, L., Wang, Z., Liu, Q., Gang, Q., & Zhang, J. (2025). Intelligent optimization of track and field teaching using machine learning and wearable sensors. Scientific Reports, 15(1), 36790. https://doi.org/10.1038/s41598-025-20745-9

Lin, K.-C., Hung, H.-C., & Chen, N. (2023). The effect of wearable technology on badminton learning performance: a multiple feedback WISER model in physical education. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00247-9

Lopez, S. (2024). Impact of Cognitive Load Theory on the Effectiveness of Microlearning Modules. European Journal of Education and Pedagogy, 5(2), 29. https://doi.org/10.24018/ejedu.2024.5.2.799

Ma, J., Ma, L., Qi, S., Zhang, B., & Ruan, W. (2025). A practical study of artificial intelligence-based real-time feedback in online physical education teaching. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-025-00411-3

Macrine, S. L., & Fugate, J. M. B. (2021). Translating Embodied Cognition for Embodied Learning in the Classroom. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.712626

Marcellis, M., Frèrejean, J., Bredeweg, B., Brand‐Gruwel, S., & Merriënboer, J. J. G. van. (2024). Motivating students in competency-based education programmes: designing blended learning environments. Learning Environments Research, 27(3), 761. https://doi.org/10.1007/s10984-024-09500-5

Mario, D. T., Komaini, A., Singh, V. K., Krishna, N., Sharma, A., Ayubi, N., Astuti, Y., Amra, F., Zalindro, A., Zulbahri, T., Marioi, A., Sepriadi, V., Singh, N. M. S. P., & Krishna, A. (2023). Journal of Physical Education and Sport, 23(12). https://doi.org/10.7752/jpes.2023.12390

Martín-Rodríguez, A., & Madrigal-Cerezo, R. (2025). Technology-Enhanced Pedagogy in Physical Education: Bridging Engagement, Learning, and Lifelong Activity. Education Sciences, 15(4), 409. https://doi.org/10.3390/educsci15040409

Mayer, R. E. (2017). Using multimedia for e‐learning. Journal of Computer Assisted Learning, 33(5), 403. https://doi.org/10.1111/jcal.12197

Mödinger, M., Wöll, A., & Wagner, I. (2021). Video-based visual feedback to enhance motor learning in physical education—a systematic review. German Journal of Exercise and Sport Research, 52(3), 447. https://doi.org/10.1007/s12662-021-00782-y

Monib, W. K., Qazi, A., & Apong, R. A. A. H. M. (2024). Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes [Review of Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes]. Heliyon, 11(2). Elsevier BV. https://doi.org/10.1016/j.heliyon.2024.e41413

Moon, J., & Park, Y. (2023). Journal of Physical Education and Sport, 23(3). https://doi.org/10.7752/jpes.2023.03080

Moro, C., Mills, K. A., Phelps, C., & Birt, J. (2023). The Triple-S framework: ensuring scalable, sustainable, and serviceable practices in educational technology. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-022-00378-y

Mostrady, A., Sanchez-Lopez, E., & Gonzalez-Sanchez, A. F. (2024). Microlearning and its Effectiveness in Modern Education: A Mini Review [Review of Microlearning and its Effectiveness in Modern Education: A Mini Review]. Acta Pedagogia Asiana, 4(1), 33. https://doi.org/10.53623/apga.v4i1.496

Mulenga, R., & Shilongo, H. (2024). Hybrid and Blended Learning Models: Innovations, Challenges, and Future Directions in Education. Acta Pedagogia Asiana, 4(1), 1. https://doi.org/10.53623/apga.v4i1.495

Naour, T. L., Hamon, L., & Bresciani, J. (2019). Superimposing 3D Virtual Self + Expert Modeling for Motor Learning: Application to the Throw in American Football. Frontiers in ICT, 6. https://doi.org/10.3389/fict.2019.00016

O’Leary, N., Wattison, N., Edwards, T., & Bryan, K. (2014). Closing the theory–practice gap. European Physical Education Review, 21(2), 176. https://doi.org/10.1177/1356336x14555300

Omarov, N., Omarov, B., Mamutov, Q., Kissebayev, Z., Anarbayev, A., Tastanov, A., & Yessirkepov, Z. (2024). Deep learning enabled exercise monitoring system for sustainable online education of future teacher-trainers. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1385205

Pandukabhaya, M., Fonseka, T., Kulathunge, M., Godaliyadda, R., Ekanayake, P., Senanayake, C., & Herath, V. (2024). Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.16168

Pham, H., Như, N. N., Luong, D., Nguyen, T., & Nguyen, V. A. L. (2024). Science mapping the knowledge base on microlearning: using Scopus database between 2002 and 2021. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/jrit-09-2023-0132

Rajabi, K. I. (2025). Beyond the LMS: How a Multi-Platform, AI-Enhanced Ecosystem Boosts Motivation, Interaction, Deep Learning, and Design Competence among Arabic-Speaking Graduate Students. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-7156104/v1

Ren, Q.-H., & Ma, Z. (2025). Experimental exploration of artificial intelligence and ADAMS simulation technology in the teaching of vertical hoop upward throw in rhythmic gymnastics. PLoS ONE, 20(9). https://doi.org/10.1371/journal.pone.0330844

Reuter, A. S., & Schindler, M. (2023). Motion Capture Systems and Their Use in Educational Research: Insights from a Systematic Literature Review. Education Sciences, 13(2), 167. https://doi.org/10.3390/educsci13020167

Samala, A. D., Bojić, L., Bekiroğlu, D., Watrianthos, R., & Hendriyani, Y. (2023). Microlearning: Transforming Education with Bite-Sized Learning on the Go—Insights and Applications. International Journal of Interactive Mobile Technologies (iJIM), 17(21), 4. https://doi.org/10.3991/ijim.v17i21.42951

Sanusi, K. A. M., İren, D., Fanchamps, N., Geisen, M., & Klemke, R. (2025). Virtual virtuoso: a systematic literature review of immersive learning environments for psychomotor skill development. Educational Technology Research and Development, 73(2), 909. https://doi.org/10.1007/s11423-025-10449-2

Skinner, A., Diller, D., Kumar, R., Cannon‐Bowers, J. A., Smith, R., Tanaka, A., Julian, D., & Perez, R. S. (2018). Development and application of a multi-modal task analysis to support intelligent tutoring of complex skills. International Journal of STEM Education, 5(1). https://doi.org/10.1186/s40594-018-0108-5

Stavrinou, L., Constantinides, A., Belk, M., Vassiliou, V., Liarokapis, F., & Constantinides, M. (2025). The Reel Deal: Designing and Evaluating LLM-Generated Short-Form Educational Videos. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2509.05962

Suo, X., Tang, W., & Li, Z. (2024). Motion Capture Technology in Sports Scenarios: A Survey. Sensors, 24(9), 2947. https://doi.org/10.3390/s24092947

Tani, G., Corrêa, U. C., Basso, L., Benda, R. N., Ugrinowitsch, H., & Choshi, K. (2014). An adaptive process model of motor learning: insights for the teaching of motor skills. PubMed, 18(1), 47. https://pubmed.ncbi.nlm.nih.gov/24314130

Wohlfart, O., Mödinger, M., & Wagner, I. (2023). Information and communication technologies in physical education: Exploring the association between role modeling and digital literacy. European Physical Education Review, 30(2), 177. https://doi.org/10.1177/1356336x231193556

Zarya, F., Wahyuri, A. S., Ihsan, N., & Batubara, R. (2023). Journal of Physical Education and Sport, 23(12). https://doi.org/10.7752/jpes.2023.12374

Zhang, J., Qu, Q., Zhu, B., Zhao, Z., & Kim, S. (2025). Microlecture Assistance during University Martial Arts Classes Improves Students’ Learning Motivation and Endeavors. Physical Culture and Sport Studies and Research. https://doi.org/10.2478/pcssr-2025-0011

Downloads

Published

2026-01-27

Issue

Section

Original Reserach

Categories

How to Cite

Saputra, S. A., Komaini, A., & Sheffield, D. (2026). Innovation in Micro-Learning Content for Physical Education Teacher Training: Integrating Motion Capture Technology to Revolutionize IT-Based Instruction. Journal of Foundational Learning and Child Development, 2(01), 06-13. https://doi.org/10.53905/ChildDev.v2i01.02

Similar Articles

11-20 of 25

You may also start an advanced similarity search for this article.