Enhancing elementary students’ computational thinking through problem-based learning in science education

Abstract

The rapid advancement of digital technology has shifted educational priorities toward the development of higher-order thinking skills, including computational thinking. However, the integration of computational thinking into elementary science learning remains limited. This study aims to examine the effect of Problem-Based Learning (PBL) on students’ computational thinking skills in science education. A quantitative approach with a quasi-experimental nonequivalent control group design was employed. The participants consisted of 40 fifth-grade students divided into an experimental group (n = 20) and a control group (n = 20), selected using a saturated sampling technique. The instrument was a descriptive test developed based on four computational thinking components: decomposition, pattern recognition, abstraction, and algorithms. Data were analyzed using normality and homogeneity tests followed by simple linear regression. The findings indicate that PBL has a statistically significant effect on students’ computational thinking skills (p < 0.05), with a coefficient of determination (R²) of 0.919. These results suggest that PBL is an effective instructional approach for fostering computational thinking skills in elementary science learning.