The Combination of Deep Learning and TGT Learning Models to Improve Learning Outcomes and Tennis Groundstroke Technique Skills - Wasis Himawanto; Haryo Mukti Widodo; Puspodari (Indonesian Journal of Educational Science (IJES), 2026)
Groundstroke is a fundamental tennis skill that requires both conceptual understanding and accurate technical execution; however, many Physical Education (PE) students experience difficulties in mastering these aspects through conventional instructional approaches. This study aimed to examine the effect of integrating the Deep Learning pedagogical approach and the Team Games Tournament (TGT) model on students’ learning outcomes and groundstroke technical skills in tennis education. A quasi-experimental posttest-only control group design was employed involving 80 PE students divided into experimental and control groups. Data were collected using a cognitive learning achievement test and a groundstroke performance assessment rubric. Descriptive statistics and the Mann–Whitney U test were used for data analysis. The results showed that the experimental group achieved higher scores than the control group in both learning outcomes and technical performance. The experimental group obtained a mean learning outcome score of 91.70 ± 6.49 compared with 65.20 ± 8.46 in the control group, while groundstroke technical performance scores were 27.60 ± 2.89 and 16.25 ± 1.21, respectively. The Mann–Whitney U test indicated significant differences between groups for learning outcomes (p < 0.001) and groundstroke technical skills (p < 0.001). These findings demonstrate that integrating meaningful learning processes with cooperative and competitive activities through Deep Learning and TGT can provide an effective instructional strategy for improving cognitive understanding and technical performance in tennis education.