Knowledge is power. 🧠
So why should you track and analyze training data?
- To understand whether your VR training fulfilled its goal.
- To understand whether something can be improved.
- To better understand your trainees and where they struggled.
- To fuel discussions regarding trainees’ experiences with VR training.
- To track how trainees improve over time.
And so on.
The data you’ll collect is real behavioural data, which tells you how trainees would react 90% of the time in real life if this situation happens. This is the most visible the first time they play, because that is when they use their emotions and instinct the most. 🧐
🛠 Use cases
Let’s look at a few use cases for different types of scenarios and how analyzing training data can help in learning how employees think in certain situations, as well as change a company’s views on how to proceed in the future.
🔥 Safety scenario
🧯 One of our clients created a safety scenario - an evacuation training for fire or smoke in the company’s building. In the scenario, trainees can see a normal working day, colleagues entering from the left side of the room and doing their work tasks, when a fire alarm goes off. At this moment, trainees are given the opportunity to leave the building via a couple of options.
🤯 After analyzing the training data, our client learned that 80% of their employees chose to leave the building the same way they entered it - via the door on the left side, or they would run wherever they see their colleagues go. What’s interesting is that the company protocol says in case of emergency, use the evacuation exit on the right side.
🤔 This means that real life situations (practice) aren’t always the same as theoretical advice. So they raised the question: “Should we retrain all of our employees or should we change the protocol instead of their behaviour?”.
👩🏻🔬 Soft-skills scenario
🤝 Many of our clients create soft-skills scenarios, where developing and training human skills are of top importance. When analyzing training data, it is more important to see that most of the trainees played the scenario more than once and chose different routes, rather than playing it once and having 5 star results.
🧐 Why, you might think. Because human behaviour is not a process or protocol, which is why making mistakes, experimenting and experiencing different consequences is good. It is how trainees learn to deal with resistance, their emotions, angry customers/colleagues, etc. Having analytics as an insight, makes it easier to conclude whether trainees are prepared for different situations or whether you should motivate them to play more times and experience more consequences.
📊 Why use analytics
In any case: It is all about improvement and training analytics are first aid to this goal.
👥 With training analytics, you will always have insights into the individual choices your trainees made. This can help you start discussions and better understand your trainees’ behaviour, thoughts and experience.
🚀 Training analytics are also important to improve yourself and your team as VR training creators. By analyzing this data and learning directly from your trainees, you can always improve the quality of the trainings you created.
💡 How to use analytics
🗣 After a training is finished, fuel a discussion in the classroom about the most and least chosen answers, by showing a visual representation of the data for each scene on a screen. You can also use the individual results as guidance to talk to particular employees and help them improve where needed.
If you’d like to collect more feedback before starting a discussion, you can always use a survey at the end of a scenario to find out more details about trainees’ experience.
👷🏼♂️ Another suggestion is to filter training data based on groups (e.g. engineers, managers, etc.) and see what each group answered the most, how each group thinks and whether the choices made depend on the job they do. This way, you know who you should focus on.
If your trainees play the same scenario again in the future, you can also track whether and how they’ve improved over time by comparing current and future results.
💡 Trainees might feel afraid of making mistakes and being watched or tracked for every choice, so in a certain VR situation they might not react as they normally would. It's important to handle data properly so that trainees will still want to follow VR trainings in the future. Let them know that making mistakes in simulations is a good thing, which helps them learn how to avoid making mistakes in real life. ✅
In case you’re using an LMS system, you can always connect it to Warp Studio and synchronize training results.
To learn more about improving retention with VR training, see the following blog post:
Interested in more?
➡️ Check which training analytics are available in Warp Studio and how to export them. ➡️ Check out our workshops and services, to either invest in your VR future or get our assistance in creating the perfect scenario!