<![CDATA[This study aims to provide an in-depth analysis of students’ computational thinking abilities in solving statistical problems by examining the differences between convergent and divergent thinking patterns. The research employed a descriptive qualitative approach involving six eleventh-grade students selected as subjects based on thinking pattern classifications, consisting of three students with a convergent tendency and three with a divergent tendency. The data collection instruments included student activity observation sheets and written tests. The data were analyzed using Miles and Huberman’s interactive model, which comprises three main stages: data reduction, data display, and conclusion drawing. The analysis focused on four core components of computational thinking: decomposition, pattern recognition, abstraction, and algorithmic thinking. The findings revealed that students with divergent thinking patterns were more active, flexible, and creative, particularly in identifying patterns, exploring multiple strategies, and constructing innovative solutions. In contrast, students with convergent thinking patterns demonstrated greater strength in systematically organizing information and applying procedures accurately, but were less thorough in exploring alternative strategies and providing broader interpretations of the analyzed data.]]>
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