Effectiveness of Kinovea Application Integrated with the PJOK GEMBIRA Framework Through a Deep Learning Approach in Volleyball Learning at Public Elementary Schools in Penyaring

Suparman; Joni Narnawan

doi:10.63922/ijebir.v5i04.5333

Suparman Sumbawa University of Technology, Sumbawa Regency, West Nusa Tenggara, Indonesia
Joni Narnawan Sumbawa University of Technology, Sumbawa Regency, West Nusa Tenggara, Indonesia

DOI: https://doi.org/10.63922/ijebir.v5i04.5333

Keywords: Kinovea Application; PJOK GEMBIRA; Deep Learning; Volleyball Skills; Mixed Methods.

Abstract

The demand for 21st-century educational innovation necessitates a paradigm shift in Physical Education, Sports, and Health (PJOK) from conventional, teacher-centered methods to interactive, student-centered learning. This study aims to describe the implementation and analyze the empirical effectiveness of the Kinovea application integrated with the PJOK GEMBIRA learning framework through a deep learning approach to improve basic volleyball skills among elementary school students. Employing a mixed-methods approach with an Explanatory Sequential design, the research utilized a quasi-experimental non-equivalent control group model for the quantitative phase, supported by qualitative observations and questionnaires. The population consisted of fifth-grade students from two public elementary schools in Penyaring, Sumbawa Regency, with a total sample of 40 students selected through total sampling (20 in the experimental group, 20 in the control group). Quantitative data analysis involved paired sample t-tests, independent sample t-tests, and N-Gain calculations, while qualitative data underwent systematic reduction and interpretation. The results demonstrate that the integrated Kinovea-PJOK GEMBIRA framework was implemented highly effectively. The experimental group's mean score increased significantly from 62.04 in the pretest to 87.59 in the posttest, yielding an N-Gain of 0.673 (Moderate-High category), whereas the control group's mean score rose from 49.44 to 65.93, with an N-Gain of 0.326. Statistical testing confirmed a significant difference between the two groups (p < 0.001). Qualitative findings revealed that 100% of students in the experimental group engaged actively, and 95% effectively utilized the Kinovea visual feedback to analyze and correct their motor mechanics. In conclusion, the systematic integration of motion-analysis technology and deep learning frameworks significantly enhances the acquisition of basic volleyball skills, offering a robust, empirical model for modernizing physical education in elementary schools.

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