Motivational and Cognitive Benefits of Training in Immersive Virtual Reality Based on Multiple Assessments

Journal of Computer Assisted Learning (2019)

Guido Makransky,
Stefan Borre-Gude &
Richard E. Mayer

The main objective of this study was to examine the effectiveness of immersive virtual reality (VR) as a medium for delivering laboratory safety training, based on multiple assessment methods.  We specifically compare an immersive VR simulation, a desktop VR simulation, and a conventional text-based safety manual.  A sample of105 first year undergraduate engineering students (49 males and 56 females) participated in an experimental design wherein students were randomly assigned to one of the three training conditions.  We include five types of learning outcomes including post-test enjoyment ratings; pre- to post-test changes in intrinsic motivation and self-efficacy; a post-test multiple choice retention test; and two behavioral transfer tests.  Results indicated that the groups did not differ on the immediate retention test, suggesting that all three media were equivalent in conveying the basic knowledge.  However, significant differences were observed favoring the immersive VR group compared to the text group on the two transfer tests involving the solving problems in a physical lab setting (d = 0.54, d = 0.57), as well as on ratings of perceived enjoyment (d = 1.44) and increases in intrinsic motivation (d = 0.69) and self-efficacy (d = 0.60).  The desktop VR group scored significantly higher than the text group on one transfer test (d = 0.63) but not the other (d = 0.11), as well as on the perceived enjoyment (d = 1.11) and increases in intrinsic motivation (d = 0.83). The results suggest that behavioral measures of transfer in realistic settings may be necessary to accurately assess the instructional value of VR learning environments.

Full Citation:
Makransky, G, Borre‐Gude, S, Mayer, RE. Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. J Comput Assist Learn. 2019; 35: 691– 707.