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<p>This mixed-methods study compared text-based Python instruction with unplugged (paper-and-pencil) activities for sixth-grade boys in mathematics. Across nine weeks per condition, intact classes engaged with parallel tasks on Sets and Integers designed to pursue dual aims: strengthening mathematical conceptual understanding and advancing core computational thinking (CT) processes. Quantitatively, groups were comparable at baseline; post-intervention, both showed significant improvement in CT, with gains favoring Python. On conceptual assessments, both conditions improved; between-group differences were evident for Integers but not for Sets, suggesting that transfer from CT to mathematics may depend on the extent to which a topic invites algorithmic structuring (e.g., conditional logic, iterative comparison). Qualitatively, ICAP coding of Python sessions (screen/audio traces and artifacts) showed a progression from Passive/Active toward Constructive/Interactive engagement, aligning process shifts with observed learning gains. Overall, the findings indicate that well-scaffolded Python activities embedded in mathematics can enhance CT and, for procedurally amenable topics, support conceptual learning—offering design guidance for integrating programming into middle-grades mathematics and a process-level account of how engagement patterns relate to learning.</p>

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Comparison of the development of computational thinking in mathematics teaching in Python supported programming and paper and pencil environments: a single-gender classroom example

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Rukiye Nur Mercan,
  • Tuğçe Kozaklı Ülger

摘要

This mixed-methods study compared text-based Python instruction with unplugged (paper-and-pencil) activities for sixth-grade boys in mathematics. Across nine weeks per condition, intact classes engaged with parallel tasks on Sets and Integers designed to pursue dual aims: strengthening mathematical conceptual understanding and advancing core computational thinking (CT) processes. Quantitatively, groups were comparable at baseline; post-intervention, both showed significant improvement in CT, with gains favoring Python. On conceptual assessments, both conditions improved; between-group differences were evident for Integers but not for Sets, suggesting that transfer from CT to mathematics may depend on the extent to which a topic invites algorithmic structuring (e.g., conditional logic, iterative comparison). Qualitatively, ICAP coding of Python sessions (screen/audio traces and artifacts) showed a progression from Passive/Active toward Constructive/Interactive engagement, aligning process shifts with observed learning gains. Overall, the findings indicate that well-scaffolded Python activities embedded in mathematics can enhance CT and, for procedurally amenable topics, support conceptual learning—offering design guidance for integrating programming into middle-grades mathematics and a process-level account of how engagement patterns relate to learning.