Evaluating Multiple Levels of an Interaction Fidelity Continuum on Performance and Learning in Near-Field Training Simulations

With costs of head-mounted displays (HMDs) and tracking technology decreasing rapidly, various virtual reality applications are being widely adopted for education and training. Hardware advancements have enabled replication of real-world interactions in virtual environments to a large extent, paving the way for commercial grade applications that provide a safe and risk-free training environment at a fraction of the cost. But this also mandates the need to develop more intrinsic interaction techniques and to empirically evaluate them in a more comprehensive manner. Although there exists a body of previous research that examines the benefits of selected levels of interaction fidelity on performance, few studies have investigated the constituent components of fidelity in a Interaction Fidelity Continuum (IFC) with several system instances and their respective effects on performance and learning in the context of a real-world skills training application. Our work describes a large between-subjects investigation conducted over several years that utilizes bimanual interaction metaphors at six discrete levels of interaction fidelity to teach basic precision metrology concepts in a near-field spatial interaction task in VR. A combined analysis performed on the data compares and contrasts the six different conditions and their overall effects on performance and learning outcomes, eliciting patterns in the results between the discrete application points on the IFC. With respect to some performance variables, results indicate that simpler restrictive interaction metaphors and highest fidelity metaphors perform better than medium fidelity interaction metaphors. In light of these results, a set of general guidelines are created for developers of spatial interaction metaphors in immersive virtual environments for precise fine-motor skills training simulations.