Analyzing Computational Thinking Skills in 7th Grade Students: A Focus on Data Processing in Mathematics Education
Abstract
Abstract: Computational Thinking (CT) skills are essential in mathematics education, particularly in data processing topics. This study aims to analyze the CT skills of 7th-grade junior high school students based on four main components: decomposing, abstraction, pattern recognition, and algorithmic thinking. A qualitative phenomenological approach was employed, involving 14 students selected purposively based on their diverse academic performance levels. Data was collected through classroom observation, CT skill tests focusing on data processing tasks, and in-depth semi-structured interviews to explore students’ problem-solving strategies and cognitive processes. The findings reveal varied CT competencies among students. For decomposing, 29% of students demonstrated high ability, effectively breaking down complex problems into manageable steps, while 36% exhibited moderate skills. In abstraction, the majority (57%) struggled to filter relevant data from irrelevant ones, highlighting this as a key area for improvement. Pattern recognition showed 36% of students in the high category, recognizing and logically explaining data trends, whereas 29% remained in the low category. Algorithmic thinking presented the strongest performance, with 43% of students categorized as high, showcasing structured and logical approaches to solving data-related problems. The study highlights the need for targeted interventions to strengthen abstraction and pattern recognition skills, crucial for comprehensive data analysis. By identifying strengths and weaknesses in CT skills, this research provides insights into designing more effective teaching strategies and developing CT-oriented curricula. The findings contribute to mathematics education by addressing 21st-century skills, equipping students with critical thinking and analytical capabilities needed in a data-driven world.
Keywords: computational thinking, mathematics, data processing, junior high school.
DOI: http://dx.doi.org/10.23960/jpmipa/v25i4.pp1809-1823
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