Improving the efficiency of virtual reality training by integrating partly observational learning

The current study hypothesized that integrating partly observational learning into virtual reality training systems (VRTS) can enhance training efficiency for procedural tasks. A common approach in designing VRTS is the enactive approach, which stresses the importance of physical actions within the environment to enhance perception and improve learning. However, based on embodied cognition theory, we hypothesized that for procedural tasks observational learning can successfully replace a portion of the active training using VRTS, reducing training time without sacrificing performance. The hypothesis was tested using a virtual reality system to train a 75-step Lego assembly task. Two conditions were compared: Active, in which the trainee had to both identify each correct brick and place it correctly in the Lego model; and Partly Observational Learning, in which the trainee was required only to select the correct brick, and then 'observed how the system automatically positioned it. Results demonstrated that training time was reduced with the incorporation of an observational learning phase, while performance time with the real Lego test as well as the number of final errors and the number of corrected errors were similar for both conditions. In conclusion, partly observational learning can enhance training efficiency for some procedural tasks without necessarily sacrificing performance if integrated properly within virtual reality training. Hence, for some procedural tasks the use of observational learning in VRTS should be encouraged.