Students’ Computational Thinking in GeoGebra-Based Geometric Transformation Learning: Analytical Dimensions of the Visual-Algorithmic Process

Geometric transformation requires students to integrate visual, symbolic, and procedural reasoning to understand changes in geometric objects. However, students recognized transformation patterns visually but struggled to translate them into systematic, mathematically justified procedures. This study aimed to analyze the relationship between visual exploration and algorithmic reasoning in GeoGebra-based geometric transformation learning, identify the visual-algorithmic gap across computational thinking categories, and identify instructional scaffolding needs. This study used an explanatory sequential mixed-methods design with 62 students. Data were collected through computational thinking tests, classroom observation, and interviews with three purposively selected students. Four computational thinking indicators: decomposition, pattern recognition, abstraction, and algorithmic thinking, were used to assess students’ competencies, while five analytical dimensions: visual exploration, pattern recognition, rule abstraction, algorithmic reasoning, and mathematical justification, were used to interpret how these competencies were manifested. The computational thinking test results showed that 12 students were in the high category, 33 in the moderate category, and 17 in the low category. High-category students consistently connected visual and coordinate patterns with general transformation rules, systematic procedures, and mathematical justification. Moderate-category students could recognize visual and coordinate patterns. The low category relied heavily on visual representations. These findings identified a visual-algorithmic gap and indicated the need for scaffolding.