Assessing Computational Thinking Skills in Learning Mechanics among Senior High School Students.

Abstract

This research explored the computational thinking (CT) abilities of Senior High School (SHS) Physics students in Jaman North District, Ghana, focusing on mechanics concepts like linear motion, circular motion, and projectiles. The study addressed three key questions: (1) the CT skills students exhibited in solving mechanics problems, (2) gender differences in CT self-efficacy, and (3) strategies students used to improve their CT skills. Anchored in Bandura’s Social Cognitive Theory, the study utilised a mixed-methods approach with a convergent parallel design. Data were collected using a mechanics concepts test with a validated rubric, a CT self-efficacy questionnaire, and semi- structured interviews. The sample comprised 156 SHS Physics students (78 males and 78 females) from Jaman North District, assessed for skills in decomposition, pattern recognition, abstraction, and algorithmic thinking, as well as CT self-efficacy. Additionally, 22 students participated in interviews to explore strategies for enhancing CT skills. Findings revealed that students generally had underdeveloped CT skills, particularly in decomposition, pattern recognition, and algorithmic thinking, with abstraction skills being relatively stronger but still weak. Both genders showed low self- efficacy in pattern recognition, abstraction, and algorithmic thinking, with females outperforming males in decomposition and males showing greater confidence in applying CT. Students reported strategies such as focused practice, collaborative learning, resource utilisation, problem structure analysis, and error reflection to improve CT skills. The study recommends incorporating CT-focused teaching methods to enhance problem- solving abilities in Jaman North District’s SHS Physics curriculum.