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Software Engineering Education (SEE) is challenging due to the inherent complexity and constant evolution of its subject. The variety of software development methodologies, tools, and approaches leads to highly heterogeneous courses, which makes hard to apply effective pedagogical strategies for core topics like programming, error detection, and requirements specification. This article reports the design and execution of a study to identify the dominant learning style among software professionals, aiming to allow teachers to tailor their methods according to students’ cognitive preferences. We administered Kolb’s Learning Style Inventory (KLSI) to 112 industry practitioners from Chile, Costa Rica, Argentina, Colombia, and Spain. Our main finding is that the “Thinking Style” is the most prevalent among respondents; characterized by a preference for analytical reasoning, logic, and structured problem-solving. This study contributes to SEE by offering evidence-based insights to improve teaching strategies.

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Mapping Kolb’s Learning Style in Industry as a Guide for Teaching in Software Engineering Education

  • 会议论文

  • 发表日期：

    2026年

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  • Mauricio Hidalgo,
  • Hernán Astudillo,
  • Laura M. Castro

摘要

Software Engineering Education (SEE) is challenging due to the inherent complexity and constant evolution of its subject. The variety of software development methodologies, tools, and approaches leads to highly heterogeneous courses, which makes hard to apply effective pedagogical strategies for core topics like programming, error detection, and requirements specification. This article reports the design and execution of a study to identify the dominant learning style among software professionals, aiming to allow teachers to tailor their methods according to students’ cognitive preferences. We administered Kolb’s Learning Style Inventory (KLSI) to 112 industry practitioners from Chile, Costa Rica, Argentina, Colombia, and Spain. Our main finding is that the “Thinking Style” is the most prevalent among respondents; characterized by a preference for analytical reasoning, logic, and structured problem-solving. This study contributes to SEE by offering evidence-based insights to improve teaching strategies.