The Effectiveness of Deep Learning Inclusion on Independence and Fitness in Junior High Students

Abstract

This study aims to analyze the effectiveness of inclusion-based adaptive physical education learning integrated with a deep learning approach in improving the independence and physical fitness of junior high school (SMP) students. The method used is a quasi-experimental design with a pretest-posttest control group design. The research sample consisted of junior high school students who were divided into experimental groups and control groups. The research instruments include physical fitness tests and independence questionnaires, which have been tested for validity and reliability. Data analysis was carried out using descriptive and inferential statistical tests through paired sample t-test and independent t-test. The results showed that there was a significant improvement in students' independence and physical fitness in the experimental group compared to the control group (p < 0.05). Deep learning-based learning in the context of inclusion has been proven to increase students' active involvement, strengthen movement understanding, and develop physical and psychosocial skills simultaneously. Additionally, this approach provides a more meaningful, adaptive, and student-centered learning experience. The conclusion of this study confirms that inclusion-based adaptive physical education learning with deep learning integration is effective in increasing the independence and physical fitness of junior high school students. These findings provide practical implications for the development of innovative and sustainable learning models in the context of physical education

Keywords

• Deep Learning Inclusion
• Adaptive Physical Education
• Student Independence
• Physical Fitness