The important thing neglected: integrating bloom's taxonomy into VR safety training model development

As an emerging technology, virtual reality (VR) is being widely used in the field of safety training. However, a potential drawback arises when the learning theory is weakly considered during the development of safety training models. This oversight may result in several defects, such as a lack of alignment with training priorities and needs, ultimately affecting trainees' learning outcomes and diminishing safety awareness. To address this issue, this study introduces a systematic framework that integrates Bloom's learning taxonomy with VR safety training model development, named the VR Safety Training Goal Taxonomy Framework (VR-Safety-TGT). VR-Safety-TGT provides a comprehensive method for safety trainers and VR model developers to communicate and understand each other's perspectives. Specifically, it assists VR model developers in better-aligning safety training learning objectives. The efficacy of VR-Safety-TGT is examined through a pilot study involving a sample of 91 university students. The students were divided into three groups, each subjected to one of the following training modalities: slideshow-based training, traditional VR training, and VR-Safety-TGT training. The experimental results reveal that the VR-Safety-TGT group exhibits a significant advantage compared to the other two groups. VR-Safety-TGT demonstrates superior outcomes in terms of knowledge acquisition, knowledge retention, self-efficacy, and users’ acceptance of technology.

Authors

Institutions

Publication Details

Journal
Virtual Reality
Published
2025-11-05
DOI
https://doi.org/10.1007/s10055-025-01255-5
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The important thing neglected: integrating bloom's taxonomy into VR safety training model development

Dirk Pons, Shuang‐Hua Yang, Chaojie Hu, Zhanni Luo et al.
Virtual Reality
Human-Automation Interaction and Safety
article

The important thing neglected: integrating bloom's taxonomy into VR safety training model development

Dirk Pons, Shuang‐Hua Yang, Chaojie Hu, Zhanni Luo, Zuzhen Ji, Shuai Zhang, Yuchen Wang, Yong Chen
article en

Abstract

As an emerging technology, virtual reality (VR) is being widely used in the field of safety training. However, a potential drawback arises when the learning theory is weakly considered during the development of safety training models. This oversight may result in several defects, such as a lack of alignment with training priorities and needs, ultimately affecting trainees' learning outcomes and diminishing safety awareness. To address this issue, this study introduces a systematic framework that integrates Bloom's learning taxonomy with VR safety training model development, named the VR Safety Training Goal Taxonomy Framework (VR-Safety-TGT). VR-Safety-TGT provides a comprehensive method for safety trainers and VR model developers to communicate and understand each other's perspectives. Specifically, it assists VR model developers in better-aligning safety training learning objectives. The efficacy of VR-Safety-TGT is examined through a pilot study involving a sample of 91 university students. The students were divided into three groups, each subjected to one of the following training modalities: slideshow-based training, traditional VR training, and VR-Safety-TGT training. The experimental results reveal that the VR-Safety-TGT group exhibits a significant advantage compared to the other two groups. VR-Safety-TGT demonstrates superior outcomes in terms of knowledge acquisition, knowledge retention, self-efficacy, and users’ acceptance of technology.

Virtual RealityVol. 29(4)
Chongqing Normal University (CN), University of Canterbury (NZ), Zhejiang University of Technology (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province
Openalex Percentile: Top 63%
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.