This research aimed to analyze computational thinking skills
from the perspective of students' self-confidence in
mathematics learning, employing a qualitative approach and a
case study method. The subjects of this research consisted of
thirty-five students from tenth grade, selected based on selfconfidence—categorized as high, moderate, and low. Data
collection techniques included a computational thinking skills
test focused on systems of linear equations in two variables, a
self-confidence questionnaire, and interviews. The findings
reveal that students with high self-confidence demonstrated
the ability to meet all indicators of computational thinking
skills. In contrast, students with moderate self-confidence
could only fulfill two indicators—decomposition and
algorithmic thinking—indicating that they struggled with
pattern recognition and abstraction. Meanwhile, students with
low self-confidence exhibited limited computational thinking
skills, with some meeting only the decomposition indicator
and others failing to meet any expected indicators.