<![CDATA[Computational thinking (CT) has emerged as a critical competency in 21st-century education; however, junior high school students’ proficiency in this skill remains insufficient, particularly in abstract scientific topics such as heat. This study aims to examine differences in students’ CT skills and to determine the effect size of implementing a Problem-Based Learning (PBL) model integrated with PhET Simulation in science learning. A quantitative approach with a quasi-experimental nonequivalent control group design was employed. The sample consisted of two seventh-grade classes, selected using cluster random sampling. Data were collected through a CT test encompassing decomposition, pattern recognition, abstraction, and algorithmic thinking, as well as an observation sheet to assess instructional implementation. Data analysis involved descriptive statistics, normalized gain (N-gain), independent samples t-test, and effect size analysis. The results indicate that instructional implementation was categorized as very good (91.5%). The mean CT score in the experimental group increased from 56.11 to 82.22, while the control group improved from 54.58 to 67.14. The independent samples t-test revealed a significant difference between groups in the posttest (p = 0.001 < 0.05). The N-gain score in the experimental group was classified as high, whereas the control group achieved a moderate level. The effect size value of 1.856 indicates a strong practical impact. These findings demonstrate that the integration of PBL with PhET Simulation effectively enhances students’ computational thinking skills. This study contributes to the theoretical advancement of CT-oriented instructional design and provides practical implications for implementing technology-enhanced science learning.]]>
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