Journal article
A Roadmap for Designing eXtended Reality tools to teach Unit Operations in Chemical Engineering: Learning Theories & Shifting Pedagogies
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark1
PROSYS - Process and Systems Engineering Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark2
Department of Chemical and Biochemical Engineering, Technical University of Denmark3
PILOT PLANT, Department of Chemical and Biochemical Engineering, Technical University of Denmark4
Knowledge Hub Zealand5
CHEC Research Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark6
This paper explores the impact of shifting from paper-based to eXtended Reality (XR) teaching tools and is informed by a design project that took place at the Chemical Engineering department at the Technical University of Denmark (DTU.CBE). The authors believe that, for operator training, teaching methods that leverage exploring, constructing and experiencing are superior to traditional teaching methods that rely heavily on conceptualising and rationalising.
Cognitive load theory is used to focus this pedagogic discussion on memory. Thereafter, a neuroscience model called SULEX is employed to decompose memory into four related cognitive functions, Perception, Instinct, Reflection and Intuition, and for mapping our learning activities to Bloom's Taxonomy.
Thereafter, another model is presented to illustrate a range of teaching discourses that can be consulted for designing XR tools for instructional purposes. For operator training, we have recommended spatial reasoning, affordance theory, distributed cognition and situated learning, among others. Finally the insights derived from the cognitive and instructional analysis will be illustrated in the presentation of several design features that were implemented as part of a Virtual Reality instructional design project at DTU.CBE.
Language: | English |
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Publisher: | Elsevier |
Year: | 2023 |
ISSN: | 27725081 |
Types: | Journal article |
DOI: | 10.1016/j.dche.2022.100074 |
ORCIDs: | Carberry, Deborah E. , Woodley, John M , Mansouri, Seyed Soheil and Andersson, Martin P. |