Analysis of Mathematical Computational Thinking Skills in Statistics Courses in Relation to High School Students' Learning Interest
TL;DR
Learning interest was associated with variations in students’ mathematical computational thinking in statistics, highlighting the importance of instructional approaches that concurrently foster learning interest and strengthen students’ computational thinking capabilities in mathematical problem solving.
Abstract
Purpose: Computational thinking is increasingly recognized as a critical competency in mathematics education, particularly for enabling students to formulate, analyze, and solve complex problems. However, students’ engagement in computational thinking may vary according to their level of learning interest. Methodology: This qualitative case study examined students’ mathematical computational thinking in statistics across different levels of learning interest. Participants were 31 eleventh-grade students at a senior high school in East Jakarta, Indonesia. Based on a learning-interest questionnaire, students were classified into high, moderate, and low interest groups, from which three representative students were purposively selected for in-depth analysis. Data were collected through a mathematical computational thinking test and semi-structured interviews and analyzed thematically using four indicators: decomposition, pattern recognition, abstraction, and algorithmic thinking. Findings: The findings demonstrated differentiated computational thinking profiles across the three levels of learning interest. The highly interested student demonstrated all four indicators and applied systematic, coherent, and logically structured problem-solving strategies. The moderately interested student exhibited adequate conceptual understanding but experienced difficulties in articulating complete and logically organized solution procedures. The student with low learning interest encountered difficulties across all four indicators, particularly in identifying relevant information, recognizing patterns, abstracting essential concepts, and constructing logical solution procedures. Significance: Within the context of this study, learning interest was associated with variations in students’ mathematical computational thinking in statistics. These findings highlight the importance of instructional approaches that concurrently foster learning interest and strengthen students’ computational thinking capabilities in mathematical problem solving.