ANALYZING STUDENT COMPUTATIONAL THINKING ABILITY IN DATA PRESENTATION BY MATH ANXIETY AND THEIR ASPECTS - Dewi, Carolina Fadia; Rizki, Nanda Arista; Kurniawan, Kurniawan; Rusdiana, Rusdiana (Universitas Sembilanbelas November Kolaka, 2025)
Computational thinking is an essential skill in mathematics learning. However, many students still face difficulties in applying its aspects. This study aims to examine differences in students’ computational thinking ability in the context of data presentation based on math anxiety levels and computational thinking aspects, as well as the interaction between the two. A quantitative approach with a comparative descriptive desig was employed. The research subjects were 119 seventh-grade students of SMP IT Cordova Samarinda, selected using purposive sampling. Research instruments consisted of a math anxiety questionnaire and a computational thinking test covering four aspects: decomposition, pattern recognition, algorithm, and abstraction-generalization. Data were analyzed using the General Linear Model with Two Way Repeated Measures ANOVA. The results revealed significant differences in cmputational thinking ability based on math anxiety level, with a tendency for lower computational thinking ability among students with high math anxiety. Significant differences were also found across computational thinking aspects, with decomposition scoring the highest and abstraction-generalization the lowest. Howeve, no significant interaction was found between math anxiety level and computational thinking aspects. These findings highlight the importance of instructional approaches that consider affective factors to optimize the holistic development of students’ computational thinking across all aspects.