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This paper presents an innovative teaching approach designed to prepare students for the demands of the modern, digitalized working world. Moving beyond traditional lecture-based courses, this method emphasizes hands-on, practice-oriented learning in interdisciplinary teams. The core of this approach involves students working with real-world demonstrators a brewing lab and a photobioreactor reactor (CO2 sink) at TU Dortmund University. These serve as platforms for students to apply theoretical knowledge in practical settings, particularly in digitizing systems using Industrial Internet of Things (IIoT) sensors. Key components of the teaching method include iterative learning, adaptive problem-solving and interdisciplinary collaboration. This paper outlines the conceptualization of this teaching method, including didactic approaches, application-related challenges, and learning objectives. It also presents a two-stage validation process involving experts and students, with the goal of incorporating this method into the curriculum as a formal course. This teaching approach aims to equip students with the skills needed to navigate rapidly evolving technological landscapes and changing workplace requirements, bridging the gap between theoretical knowledge and practical application in data science and IIoT.

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Bridging the Digital Skills Gap: A Hands-On Iterative Learning Factory Model for Next-Generation Data Science and IIoT Skills

  • 会议论文

  • 发表日期：

    2026年

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  • Jochen Deuse,
  • Roman Möhle,
  • Daniel Boiar

摘要

This paper presents an innovative teaching approach designed to prepare students for the demands of the modern, digitalized working world. Moving beyond traditional lecture-based courses, this method emphasizes hands-on, practice-oriented learning in interdisciplinary teams. The core of this approach involves students working with real-world demonstrators a brewing lab and a photobioreactor reactor (CO2 sink) at TU Dortmund University. These serve as platforms for students to apply theoretical knowledge in practical settings, particularly in digitizing systems using Industrial Internet of Things (IIoT) sensors. Key components of the teaching method include iterative learning, adaptive problem-solving and interdisciplinary collaboration. This paper outlines the conceptualization of this teaching method, including didactic approaches, application-related challenges, and learning objectives. It also presents a two-stage validation process involving experts and students, with the goal of incorporating this method into the curriculum as a formal course. This teaching approach aims to equip students with the skills needed to navigate rapidly evolving technological landscapes and changing workplace requirements, bridging the gap between theoretical knowledge and practical application in data science and IIoT.