The Effect of the SSCS Model on Computational Thinking Skills in Linear Motion

Abstract

This study aims to examine the effect of the SSCS (Search, Solve, Create, and Share) learning model on improving computational thinking skills in the linear motion topic. This study employed a mixed-methods design, combining quantitative research with qualitative data from learning video transcript analysis. The research participants were 32 experimental class students and 34 control class students at a high school in Cimahi, selected through a purposive sampling technique based on their enrollment in physics and the linear motion topic. The participants were divided into an experimental class taught using the SSCS model and a control class taught using a discovery learning model. Data collection was carried out using test instruments to measure the improvement in computational thinking skills and learning video transcripts. Quantitative analysis using N-gain and t' test (due to normally distributed non-homogeneous data) and qualitative data were analyzed by providing coding on learning transcripts. The hypothesis test results showed that the application of the SSCS model had a significant effect on improving computational thinking skills compared to the control class, with t~count~ = 6.46 > t~table~ = 2.00. The N-gain score for the experimental class reached 0.54 (medium category), while the control class only reached 0.22 (low category). The highest improvement in the experimental class occurred in the abstraction and decomposition indicators, whereas the control class experienced a decline in the algorithmic skills indicator. The qualitative analysis supports these findings, demonstrating that SSCS syntax fosters computational thinking skills during learning, particularly in the create phase, which extensively trains algorithmic thinking and evaluation