Analysis of Students' Computational Thinking Abilities Based on Self-esteem

Abstract

Computational Thinking skills are a fundamental 21st-century skill that is essential in solving mathematical problems, but its success is inseparable from affective factors such asSelf-esteem. This research is important because there are still few in-depth qualitative studies that analyze the CT thinking process from the characteristics ofSelf-esteem. Restriction on categories onlySelf-esteemhigh and low (without medium category) are based on theory Rosenberg (1965). The purpose of this study is to describe the CT ability of students reviewed fromSelf-esteemhigh and low. The method used was qualitative exploratory with the subjects of four students of class IX C SMP Negeri 5 Tasikmalaya who were selected purposively (two withSelf-esteemtall and two withSelf-esteemlow) and able to meet all four CT indicators. Instruments include questionnairesSelf-esteemGuttman scale, written tests on algebraic form stories, and semi-structured interview guidelines. Data collection was carried out through questionnaire distribution, CT tests, and in-depth interviews, while data analysis used the Miles & Huberman analysis framework (reduction, presentation, conclusion drawn) with triangulation techniques. The results showed that cognitively all subjects were able to meet all four CT indicators, but the tendency of their thought processes to differ qualitatively. Students withSelf-esteemhigh tends to indicate a systematic, structured, and efficient thought process (for example, students tend to understand the problem immediately with confidence without difficulty simplifying algebra, or tend to be more careful by double-checking). In contrast, students withSelf-esteemtend to be nervous, afraid of making mistakes, read questions repeatedly, and hesitating in determining patterns, and also tend to panic at the beginning, forget formulas, and have time to ask friends. Implications,Self-esteemplays more role in shaping the quality of computational thinking experiences than simply determining the achievement of cognitive indicators