Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/6968
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dr. KWOK Pak Ki, Alex | en_US |
dc.contributor.author | Yan, Mian | en_US |
dc.contributor.author | Deng, Xin Hai | en_US |
dc.contributor.author | Chen, Xiao Yu | en_US |
dc.contributor.author | Huang, Ying Ting | en_US |
dc.date.accessioned | 2022-03-25T02:29:12Z | - |
dc.date.available | 2022-03-25T02:29:12Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Computer Applications in Engineering Education, 2022, vol. 30(4), pp. 1072-1085. | en_US |
dc.identifier.issn | 1061-3773 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/6968 | - |
dc.description.abstract | Virtual reality (VR) learning is still a new challenge in the e-learning domain. This paper is one of the early studies bringing forward the concept of learning 5S principles (a core technique in Industrial Engineering) using VR and performed an exploratory qualitative study in an undergraduate course to discover factors that would facilitate or hinder students from accepting this idea—learning 5S principles using VR. Four factors and 14 subfactors were identified based on 47 valid responses. They were human attributes, human–machine interaction, technology characteristics, and training contents. The results should help teachers, schools, VR device manufacturers to understand the benefits and challenges of teaching 5S principles using VR. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Computer Applications in Engineering Education | en_US |
dc.title | Exploring the facilitating and obstructing factors of using virtual reality for 5S training: An exploratory qualitative study from students' perspectives in an industrial engineering undergraduate course | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1002/cae.22503 | - |
crisitem.author.dept | Department of Applied Data Science | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | Applied Data Science - Publication |
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