This is a question we get asked a lot, and rightly so. However, it’s not actually a very useful question.
What is more useful is to ask is: ‘Which factors have the greatest impact on effectiveness?’, and ‘How well can these factors be controlled given real-world constraints?’ (e.g. budgets, technology limits). We will provide a full answer to these very questions in our upcoming whitepaper, but here is a short preview.
The basis of most learning (at a behavioural level), are experiences with cause and effect. A person observes relationships between ‘variables’ (e.g. people, events, their actions, etc) in their environment and starts to build causal models about how the world works. These models form the basis of what is colloquially termed ‘experience’. A high level of domain-specific experience (e.g. in leadership) is what gives rise to a person becoming an ‘expert’ or SME.
Unsurprisingly then, one of the key drivers of effective learning in VR (and other modalities), is the opportunity for a learner to build new, and refine existing, domain-specific causal models. One of the main ways this occurs is through meaningful interactions with variables relevant to the domain of learning. For the leadership example, this could mean opportunities to apply your own actions, be they physical (body language), verbal (tone of speech) or cognitive (planning/decisions), to the variables in your environment (other people, workplace problems) and receiving feedback on these.
In a VR learning context, this means that the effectiveness of a VR learning experience is partly dependent on the quality (and to a lesser extent, the quantity) of these interactions.
Meaningful can be defined as an interaction (physical, verbal, cognitive) that helps the learner build/refine their causal model in the domain they are learning. For example, being able to pick up and throw around the hard hat and safety gloves within a VR simulation that is trying to train hazard awareness, is not meaningful. Yes, it will help the learner build their causal model of basic physics, but not much beyond that (novelty factor aside). This interaction is not likely to elicit thought processes or behaviours related to scanning the environment for safety hazards, nor does it provide relevant feedback on such behaviour.
A better option in this example might be to pose an instruction such as:
“If you think it is safe to do so, walk to the door”
This would then elicit a whole host of cognitive processes (e.g. are there any hazards?, are they between me and the door etc?) and behaviour (looking). After deciding and acting, the learner could be provided with a powerful form of feedback, such as an object nearly falling on them if they chose incorrectly.
A more meaningful interaction such as this would help a learner refine their causal model about hazards in a particular environment (and likely be more cost-effective to design than modelling interactive objects). This in turn will make the experience more effective.
Fortunately, one of the main advantages of VR is that a high level of interactivity (and realism, another moderator of effectiveness) is relatively fast and affordable to achieve compared with other learning modalities. Our learning creator platform Facilitate, aims to do just that, allowing you to create VR learning experiences that are effective and to do so easily and affordably.
Ultimately, the experiences a person has is what will lead to behaviour change. VR is merely the technological vehicle to provide these experiences in a cost-effective manner.