Traditional Games Based Deep Learning to Improve Physical Literacy in Elementary School Students
Abstract
This study aimed to analyze the effect of traditional games based on deep learning on elementary school students’ physical literacy. Physical literacy is an important component in physical education because it integrates physical competence, motivation, confidence, knowledge, and social interaction in lifelong physical activity. However, physical education learning in elementary schools is often still focused on technical mastery and has not optimally provided meaningful and engaging learning experiences for students. Therefore, traditional games integrated with deep learning approaches were implemented to create active, reflective, and enjoyable learning experiences. This study used an experimental method with a pretest-posttest non-equivalent control group design involving 72 fourth-grade elementary school students. The participants were divided into two groups, namely the galah traditional game group and the sondah traditional game group. Data were collected using the Physical Literacy in Children Questionnaire based on the Australian Physical Literacy Framework. The data were analyzed using descriptive statistics, paired sample t-test, and independent sample t-test. The results showed that both traditional games significantly improved students’ physical literacy (p < 0.05). However, the galah traditional game group demonstrated a higher increase compared to the sondah group. The findings indicate that traditional games involving dynamic movement, teamwork, social interaction, and strategic decision-making provide more meaningful movement experiences and contribute more optimally to the development of students’ physical literacy. Therefore, traditional games based on deep learning can be used as an innovative and effective approach in elementary school physical education learning.
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