Immersive virtual reality (IVR) refers to computer-generated real-time, three-dimensional and interactive environments. Their use is associated with diverse potentials, such as spatially and temporally independent training. To assess the effectiveness of an immersive virtual learning environment, it is particularly important to understand the extent to which virtually learned content can be applied practically. This aspect is referred to as learning or training transfer (Bossard, Kermarrec, Buche, & Tisseau, 2008; Rose, Attree, Brooks, Parslow, & Penn, 2000). However, to our knowledge, no study to date in the field of mechanical and plant engineering has yet investigated whether people are able to operate a real mechanical machine after completing virtual training. In this study the method of video analysis was used for the first time to meet this research desideratum (Vid-VR) and formatively evaluate a virtual learning environment.
A group of people from the technical field of mechanical and plant engineering (N = 13) participated in virtual operator training. The immersive learning sequence in self-learning mode comprises of a total of 36 instructed action steps. Learners can virtually ‘remove the construction cylinder’ at a machine for the additive production of complex, metallic components. To formatively evaluate the virtual learning environment the participants answered quantitative questionnaires on aspects of technology acceptance, motivation, physical and cognitive effort, user experience, and subjective learning success. Furthermore, they were asked to apply what they had learned virtually to the real machine and remove the real construction cylinder. Both the virtual training and the testing phase on the real machine were recorded by video (800 minutes in total). A structured qualitative video analysis was conducted, which is oriented in its basic approach to Derry and colleagues (2010) and the structured qualitative content analysis according to Mayring (2014). Figure 1 shows the schematic procedure Vid-VR.
Figure 1: Schematic procedure of coupled video analysis to the immersive virtual training and testing phase (Vid-VR)
The category system resulting from the structured qualitative video analysis contains design-, instruction- and interaction-related optimisation potentials for further development of the virtual learning sequence. Mistakes, difficulties and other anomalies during the application on the real machine provide further revision options. However, it should be noted that most of the virtually learned action steps could be transferred to real activity and no errors or difficulties were found regarding the spatial arrangement of the machine or the identification and location of components. The quantitative descriptive results show positive assessments.
In summary, this study addresses the research desideratum after evaluation studies on virtual learning environments in the technical field. The methodical approach using video analysis can offer a useful basis for other research groups as it seems to be easily transferable and specifically extendable to further IVR applications to evaluate them. In accordance with the literature, IVR showed advantages in explaining spatial relationships, but limitations in haptics. The results can be considered in the context of the training concept of immersive virtual training (Zinn, Pletz, Guo, & Ariali, 2020).
This blog is based on the article ‘Evaluation of an immersive virtual learning environment for operator training in mechanical and plant engineering using video analysis’ by Carolin Pletz and Bernd Zinn, published on an open access basis in the British Journal of Educational Technology.
Bossard, C., Kermarrec, G., Buche, C., & Tisseau, J. (2008). Transfer of learning in virtual environments: A new challenge? Virtual Reality, 12(3), 151–161.
Derry, S. J., Pea, R. D., Barron, B., Engle, R. A., Erickson, F., Goldman, R., Hall, R., Koschmann, T., Lemke, J. L., Gamoran Sherin, M., & Sherin, B. L. (2010). Conducting video research in the learning sciences: Guidance on selection, analysis, technology, and ethics. Journal of the Learning Sciences, 19(1), 3–53.
Mayring, P. (2014). Qualitative content analysis: Theoretical foundation, basic procedures and software solution. Klagenfurt: Alpen-Adria University.
Pletz, C., & Zinn, B. (2020). Evaluation of an immersive virtual learning environment for operator training in mechanical and plant engineering using video analysis. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13024
Rose, F. D., Attree, E. A., Brooks, B. M., Parslow, D. M., & Penn, P. R. (2000). Training in virtual environments: Transfer to real world tasks and equivalence to real task training. Ergonomics, 43(4), 494–511.
Zinn, B., Pletz, C., Guo, Q., & Ariali, S. (2020). Conceptual design of virtual teaching and learning arrangements in the context of the industrial service industry of machine and plant engineer. In B. Zinn (Ed.), Virtual, augmented and cross reality in practice and research: Technology-based experience worlds in vocational education and training – theory and application (pp. 169–184). Stuttgart: Franz Steiner Verlag.