Computational thinking Skills from the perspective of students’ self-confidence in mathematics learning

Abstract

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 self-confidence—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.