Planning Learning Data Use in VR

How will you determine success through the learning data you receive from your VR training scenarios? After setting training goals, you will need to determine how you will measure and track your training metrics. Specifically, you must decide what it looks like to achieve success on the performance indicators you have outlined and how that data will be stored, tracked and displayed. Let’s discuss how to plan learning data use in VR.

The types of learning data to extract from your VR training program

Many VR experiences can generate robust and rich data sets. The challenge is determining the data that best illustrates the key performance indicators (KPIs) you have set for the training.

xAPI

At a basic level, Experience Application Programming Interface data, more commonly known as xAPI data, is a collection of statements describing actions taken during the training scenario:

  • “Learner looked at object”
  • “Learner watched video”
  • “Learner used object”
  • “Learner spoke to avatar”

You can collect information on completion of the events and sometimes even their duration. The statements become meaningful when learning designers determine the sequence and frequency of the events that represent successful completion of the activity. You can determine scenario success based on your predetermined KPIs.

For example, if we look at the following KPI, “Learner will scrub all widgets in the inner chamber a minimum of 10 times using the Foodbot cleaning solvent”, you could specifically track the statement “Learner used cloth on widget” and define that usage 10 times as a “success”.

Gaze and motion data

In addition to xAPI, you can also extract information about where the learner looks and how they move. Analysis of this data by researchers has revealed that the way a learner moves and interacts in their environment can be a very strong indicator of their proficiency. The implications of this are potentially transformative for training assessment and could drastically alter the way workplaces determine job readiness and skill level.

This level of data collection, analysis and interpretation would likely require involvement from an outside research team, but the implications of this field of study are very exciting. In the not-so-distant future, employers will be able to run their trainees through VR scenarios and immediately know if they are job ready or if they need more training. This kind of on-demand, unbiased information has never been available before.

Planning learning data use through graphs.
Planning learning data use.

Storage and visualization of your learning data

With a data plan in place, you will need to ensure that you have a way to store and visualize the learning data you collect. Your company’s learning management system may have these capabilities, but you may also consider using a learning records store (LRS) to get more from your data. This website lists all LRS solutions that have been deemed conformant by the Advanced Distributed Learning Initiative.

The data from your VR training sessions is important in understanding whether or not your employees are ready to be put on the job. Establishing the data you will track against your KPIs will be essential to monitoring employee readiness. Finally, determining a place to store, track, and visualize this data will be an important part of your VR training data plan.

Want to learn more about VR training and how to get started?
Download our guide: VR Training Information and Decision Making Guide.

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Sara Johnston

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