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The instructional laboratory’s role in engineering education is critical, yet its fundamental objectives often fail to foster deep learning. Feisel and Rosa’s (2005) taxonomy, a key framework for designing and reflecting on laboratory experiences in engineering education, outlines thirteen Fundamental [Learning] Objectives derived from an expert workshop. However, these objectives, as originally formulated, do not consistently promote deep learning. This paper presents the first step to create a refined version of this taxonomy, aimed at enabling deep learning across all objectives. Utilizing a deductive qualitative content analysis informed by the SOLO Taxonomy—a model that categorizes learning outcomes from simple to complex—this revision focuses on the use of specific verbs and attributes that align with higher levels of understanding (Relational and Extended Abstract Understanding). The revised taxonomy ensures that each learning objective is crafted to enhance deep learning, potentially improving both the planning and evaluation of educational effectiveness. The anticipated outcome is a more effective framework that can serve not just as a theoretical guide, but also as a practical tool in shaping engineering education to meet contemporary demands. This approach not only repairs the “broken jug” of the current taxonomy but replaces it with one more attuned to the needs of deep learning, suggesting that educational tools must evolve alongside educational goals.

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A Jug Goes So Often to the Well Until it Breaks: Refurbishing the Thirteen Fundamental Objectives of Instructional Laboratories by Integrating the SOLO Taxonomy

  • 会议论文

  • 发表日期：

    2025年

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  • Marcel Schade,
  • Claudius Terkowsky,
  • Konrad Boettcher,
  • Nils Kaufhold,
  • Tobias R. Ortelt,
  • Dominik May,
  • Alexander S. Behr,
  • Valerie Varney,
  • Uwe Wilkesmann

摘要

The instructional laboratory’s role in engineering education is critical, yet its fundamental objectives often fail to foster deep learning. Feisel and Rosa’s (2005) taxonomy, a key framework for designing and reflecting on laboratory experiences in engineering education, outlines thirteen Fundamental [Learning] Objectives derived from an expert workshop. However, these objectives, as originally formulated, do not consistently promote deep learning. This paper presents the first step to create a refined version of this taxonomy, aimed at enabling deep learning across all objectives. Utilizing a deductive qualitative content analysis informed by the SOLO Taxonomy—a model that categorizes learning outcomes from simple to complex—this revision focuses on the use of specific verbs and attributes that align with higher levels of understanding (Relational and Extended Abstract Understanding). The revised taxonomy ensures that each learning objective is crafted to enhance deep learning, potentially improving both the planning and evaluation of educational effectiveness. The anticipated outcome is a more effective framework that can serve not just as a theoretical guide, but also as a practical tool in shaping engineering education to meet contemporary demands. This approach not only repairs the “broken jug” of the current taxonomy but replaces it with one more attuned to the needs of deep learning, suggesting that educational tools must evolve alongside educational goals.