Abstract
Digitalization, smartification and cyber-physical systems are going to reshape the working lives of future engineers. Thus, these developments will have an impact on higher education in the near future. For years, there has been a vivid discussion on the respective competencies students need to develop in order to be able to successfully face these challenges. In this article, the laboratory is presented as a cyber-physical system that can offer a setting to foster these competencies in engineering laboratory courses. We present the remote laboratory as a setting in which future-oriented teaching and learning in the light of required competencies for the ‘working world 4.0’ can take place. In order to identify these competencies and to analyze an existing laboratory, a qualitatively oriented content-analytical procedure is used. In a first step, based on current scientific studies, competencies expected to be shown by future engineers are named and summarized. In a second step, an exemplary existing remote laboratory is analyzed with regard to explicit and implicit learning objectives, which address the identified competency requirements. It can be shown that only a few professional and interdisciplinary competencies in the context of Industry 4.0 are being addressed so far. Based on the focused example, the close connection to engineering fundamentals on the one hand and the lack of interdisciplinarity in the observed learning scenario on the other hand can be identified as critical parameters, which limit the promotion of more future-oriented competencies. This shows possibilities for future research and development in this area.
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Acknowledgment
The presented work was done in the scope of the research project “ELLI – Excellent Teaching and Learning in Engineering Sciences”, funded by the German Ministry of Education and Research (project number: 01PL16082).
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Terkowsky, C., Frye, S., May, D. (2021). Using Constructive Alignment to Evaluate Industry 4.0 Competencies in Remote Laboratories for Manufacturing Technology. In: Auer, M., May, D. (eds) Cross Reality and Data Science in Engineering. REV 2020. Advances in Intelligent Systems and Computing, vol 1231. Springer, Cham. https://doi.org/10.1007/978-3-030-52575-0_50
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DOI: https://doi.org/10.1007/978-3-030-52575-0_50
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