With Virtual Reality training, companies can access data that has gone largely untapped to this point. Thanks to complete immersion in the experiences, learners’ actions are captured and the cumulative data collected provides insights into behavior that traditional training methods never have.
Exploring the 5 Types of VR Training Data
01
Usage data: The foundation of training data
Usage data is essential information: who is using the training, how often and for how long. Ultimately, usage data tells us more about the learner completing a specific training. Although this information isn’t unique to VR, usage data gives us insights into completion and repetition rates at scale to inform future training strategies.
02
Performance data: A more valid reflection of real-world behavior
Performance data demonstrates how good trainees are at a given skill. Since immersive experiences require 360-degree exploration and interaction, they test learners’ expertise in a more sophisticated and realistic way, which leads to a more accurate picture of how they will act in the real world.
03
Attention and engagement data: Indicators of on-the-job performance
Attention and engagement data indicate where and how trainees pay attention by capturing head and hand movement, interactions, and clicks. This “immersive” data is unique to VR, and with virtual environments closely simulating the real world, behaviors during training—decision- making, reaction times, head movement—are more reflective of real-world behavior.
04
Predictive analytics: The next frontier of Immersive Learning
Predictive analytics are a combination of performance and engagement data mapped to real-world data to create a machine learning-based predictive model. With predictive analytics, training performance is used to predict future on- the-job behavior and customer throughput. This helps limit the guesswork involved in whether an employee is ready for a job, as well as in hiring and promoting.
05
Sentiment analysis: Empowering employee feedback and insights
Sentiment analysis shows how users feel about an experience in their own words. This is often collected by asking open– ended questions after the VR experience, such as: how will this training be useful in your job? What did you enjoy about the training? How will this training help you feel more prepared for work?
The 5 types of data allow L&D leaders to shape improvements for future experiences.
More insights with VR training data
Combining performance data with attention and engagement data helps paint a holistic picture of workforce proficiency and predict on-the-job performance. Equipped with this data, you can iterate to enhance the VR training and make it even better and more effective. But more importantly, it helps you make better people decisions that ultimately affect large-scale business objectives.
About the author:
Michael Casale, PhD
Chief Science Officer, Strivr
Dr. Michael Casale is a cognitive neuroscientist whose academic research focused on understanding the biological underpinnings of learning and memory, in particular how to optimize training for a variety of learning situations.