Applying the Computational Thinking for Science Framework to Enhance Students’ Computational Thinking in Renewable Energy Topics

Abstract

This study investigates the effectiveness of the Computational Thinking for Science (CT-S) framework in improving high school students’ computational thinking (CT) skills in renewable energy learning. Results indicate that CT-S–based instruction resulted in marked improvement in students’ computational thinking performance. The study employed an embedded mixed-method design in which qualitative data served as the primary source of evidence, supported by quantitative analysis. The quantitative component used a pre-experimental one-group pretest–posttest design. Rasch modelling was applied to examine changes in item difficulty, person ability, and CT ability levels. Qualitative data were collected through Verbal Protocol Analysis (VPA) to capture students’ cognitive processes during learning activities. CT-S–aligned activities engaged students in data analysis, modelling simulations, solar-panel experimentation, and the design of an automated solar-tracking system. Qualitative findings show progressive development of CT components. Abstraction dominated early data interpretation, while decomposition and algorithmic thinking emerged during modelling and experimentation. Advanced problem-solving became evident during the engineering design activity. Quantitative results confirmed substantial improvement. Mean item difficulty decreased from 0.25 to –0.33 logits, mean person ability increased from 0.04 to 0.56 logits, and the number of students reaching the highest CT level increased from 10 to 31, with no decline observed. Overall, the findings demonstrate that CT-S–based instruction effectively strengthens students’ conceptual and procedural computational thinking skills in science learning.