<![CDATA[Computational thinking (CT) plays an important role in supporting students’ mathematical problem-solving abilities, particularly in technology-assisted learning environments. This study aimed to analyze students’ computational thinking profiles in solving linear programming problems using the simplex method based on their prior mathematical ability and to examine their responses to the use of POM-QM for Windows as a learning support tool. A qualitative descriptive approach was employed involving 21 undergraduate students in a mathematics education program. Three students representing high, moderate, and low levels of prior mathematical ability were purposively selected for in-depth analysis. Data were collected through linear programming problem-solving tasks assisted by POM-QM for Windows, semi-structured interviews, and student response questionnaires. Data analysis was conducted through data reduction, data display, and conclusion drawing based on four computational thinking components: decomposition, pattern recognition, abstraction, and algorithmic thinking. The findings showed that students’ computational thinking profiles differed according to their prior mathematical ability. Students with high prior mathematical ability demonstrated all CT components consistently through systematic problem decomposition, accurate mathematical modeling, effective pattern identification, and appropriate application of simplex procedures. Students with moderate prior mathematical ability showed adequate performance but experienced difficulties in pattern recognition and algorithmic implementation. Meanwhile, students with low prior mathematical ability encountered challenges in identifying relevant information, constructing mathematical representations, and developing structured solution procedures. Students generally responded positively to the use of POM-QM for Windows, particularly in simplifying calculations and supporting the problem-solving process. These findings indicate that prior mathematical ability influences students’ computational thinking performance and highlight the importance of integrating technological tools with mathematical reasoning in learning activities.]]>
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