The Effectiveness of a Deep Learning Model Supported by Smart Tools in Learning the Volleyball Serve Skill Among Students

Section: Physical Education and Sport Sciences
Issue
Vol. 29 No. 91 (2026): VOL.29 NO.91
Article History
Received: 2026-04-28
Revised: 2026-06-04
Accepted: 2026-06-21
Published: 2026-10-15
Pages
95-113

Abstract

The deep learning model, supported by smart tools, is one of the modern models used in teaching physical education, especially in volleyball, due to the rapid development in artificial intelligence technologies and the technologies used in sports teaching and training in the modern world. The aim of this study was to investigate the effect of using a deep learning model supported by smart tools in teaching the volleyball serving skill to second-year students at the College of Physical Education and Sports Sciences, University of Anbar. The researcher adopted the experimental method with a design of two equivalent groups (experimental and control). The sample consisted of 20 students, equally divided into two groups: the experimental group was taught using the deep learning (smart) model, while the control group was taught using the traditional method. The educational program consisted of six instructional units, with each unit lasting 90 minutes, and was implemented during the second semester of the 2025–2026 academic year.


The results revealed statistically significant differences between the pre-test and post-test in favor of the experimental group across all study variables, while no significant differences were observed in the control group. The findings indicate that integrating deep learning techniques and smart tools into sports education improves technical and physical performance, shortens learning time, and reduces instructional errors. This makes the model an effective approach to keeping pace with global developments in sports education.

Keywords:

Deep learning model, Smart tools, Serving skill, volleyball

References

  1. - References
  2. [1]. Al-Saeed, A. (2020). Artificial intelligence and its applications in physical education. Cairo: Dar Al-Fikr Al-Arabi. p65, 89, 107 [In Arabic]
  3. [2]. Alian, A. R., Ahmed, K., & Hamza, Y. (2000). Scientific research methods: Foundations and applications. Amman: Dar Al-Fikr.p176, 86. [In Arabic]
  4. [3]. Al-Shammari, S. (2018). Basic skills in volleyball: Educational and training foundations. Mosul: Dar Ibn Al-Atheer. P96, [In Arabic]
  5. [4]. Hassan, R. (2019). Traditional education and its problems in the sports field. Cairo: Anglo-Egyptian Library. p79. [In Arabic]
  6. [5]. Al-Sayed, A. M. (2024). Developing Physical Education Curricula in the Age of Artificial Intelligence, published in the Journal of Sports Science and Arts, 28(3), p115-132. [In Arabic] https://www.researchgate.net/publication/382317461_ttwyr_mnahj_altrbyt_alryadyt_fy_sr_aldhka_alastnay
  7. [6]. Hassan, M. Abdullah. (2024). The Impact of Using Virtual Reality Technology on Learning the Overhand Serve in Volleyball, published in the Journal of Comprehensive Education Research, 12(4), p201-218. [In Arabic] https://journals.ekb.eg/article_414004.html
  8. [7]. Omran, H. M., & Al-Fartousi, A. H. (2026). The effect of educational units based on the Murder strategy on learning the skills of serving and spiking in volleyball, a research paper published in the Journal of Physical Education Sciences, 19(2). p88-104 [In Arabic] (https://mjss.uomustansiriyah.edu.iq/index.php/mjss/article/view/1795)
  9. [8]. Abbas, N. I., & Rashid, M. Abdul-Amir. (2026). The effect of Posner's model on mental flexibility and learning the skills of serving and receiving serves in volleyball for female students, a research paper published in the Journal of Physical Education Studies and Research, 36(1). [In Arabic]
  10. [9]. Hassanein, M. S. (1995). Assessment and measurement in physical education. Cairo: Dar Al-Fikr Al-Arabi. p106 [In Arabic]
  11. [10]. Alawi, A., & Radwan, M. N. (2001). Motor performance tests. Cairo: Dar Al-Fikr Al-Arabi. p80. [In Arabic]
  12. [11]. Kautz, S. (2021). Deep learning applications in volleyball tactical analysis: A new era of performance monitoring. International Journal of Computer Science in Sport, 20(1), 45–60. doi.org p102, 95.
  13. [12]. Jidovtseff, B., Valdes, J., & Wang, L. (2021). The use of smart technologies and sensors in volleyball training: A systematic review. Journal of Sports Sciences, 39(8), 912–925. doi.org, p130.
  14. [13]. Abdul-Amir, H., & Al-Jubouri, A. (2022). Digital technologies and artificial intelligence in sports scientific research. Baghdad: Dar Al-Kutub Wal-Watha'iq. p84. [In Arabic]
  15. [14]. Al-Saeed, M. (2023). The impact of artificial intelligence on motor learning and biomechanical efficiency in team sports. Journal of Sports Technology and Innovation, 4(2), p65.
  16. [15]. Hussein, H. N., & Abdul-Musleh, W. J. (2020). The effect of skill exercises using infographic technology on students' learning of the volleyball serve skill. Al-Fath Journal for Educational and Psychological Research, 24(4). p15. [In Arabic]
  17. [16]. Mohammed, J. K., & Nouri, S. G. (2025). The effect of the HIIDA TABA model on learning the overhand forward serve skill in volleyball for students. Journal of Educational Research, 30(3). p17. [In Arabic]
  18. [17]. Naji, S. A., & Abdul Qader, N. K. (2025). The role and scope of artificial intelligence in enhancing volleyball learning. July Champions Journal, 3(2).
  19. [18]. Sadoon, A. (2020). Using the ASSURE model in teaching the overhand and underhand serves skills in volleyball for students. Wasit Journal of Humanities and Social Sciences.p86.
  20. [19]. Al-Khalidi, J. (2022). Applications of artificial intelligence in sports sciences: A future vision for digital training. Amman: Dar Al-Manahij for Publishing and Distribution. p94. [In Arabic]
  21. [20]. Zheng, L., et al. (2023). Deep learning in sports biomechanics: Analysis of kinematic chains in overhead skills. Journal of Advanced Sport Technology, 7(1), p15–29.
  22. [21]. Miller, R., & Thompson, S. (2024). Artificial intelligence and motor mastery: The evolution of feedback systems in team sports. International Journal of Digital Sports Science, 12(3), p104.
  23. [22]. Zanevskyy, I., & Zanevska, L. (2018). Computer modeling of human motion in sports. Journal of Physical Education and Sport, 18(2), p87.
  24. [23]. Wang, J., et al. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, p69.
  25. [24]. Al-Khouli, M. (2021). Sports technology and digital motor learning. Riyadh: International Publishing House. p98. [In Arabic]
  26. [25]. Bartlett, R. (2014). Introduction to sports biomechanics: Analysing human movement patterns. Routledge.p 97.
  27. [26]. Jassim, A., & Al-Tamimi, M. (2023). Biomechanics and digital analysis in team games. Basra: University of Basra Press. P 96. [In Arabic]
  28. [27]. Al-Kinani, H. (2022). Artificial intelligence applications in volleyball training: Kinetic energy and ball velocity analysis. Journal of Sport and Technology Research, 5(1), p 101.
  29. [28]. Chen, X., et al. (2021). Deep learning in athletic performance: Improving speed and precision through real-time analysis. IEEE Transactions on Sport Science, 14(2), p 106.
  30. [29]. Azmi, M. (2019). Physical measurements and digital evaluation in physical education. Riyadh: Dar Al-Nashr Al-Ilmi.p 96. [In Arabic]
  31. [30]. Gomez, F., & Santos, R. (2024). The future of neural networks in biomechanical performance optimization. International Journal of Computer Science and Sports Performance, 16(4), p74.

Author

Adnan Abduljabbar Shahatha

General Directorate of Anbar Education

Identifiers

DOI: 10.33899/yq841s59

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The Effectiveness of a Deep Learning Model Supported by Smart Tools in Learning the Volleyball Serve Skill Among Students. (2026). Al-Rafidain Journal For Sport Sciences, 29(91), 95-113. https://doi.org/10.33899/yq841s59
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How to Cite

The Effectiveness of a Deep Learning Model Supported by Smart Tools in Learning the Volleyball Serve Skill Among Students. (2026). Al-Rafidain Journal For Sport Sciences, 29(91), 95-113. https://doi.org/10.33899/yq841s59