Understanding and analyzing data from a VR training scenario allows us to address several questions:

• Did the VR training achieve its intended objectives?
• What improvements can be made to the VR training?
• Were there any participants who found it difficult to complete the VR training?
• How do participants' performances evolve with time?
• What other insights can be gleaned from the training data?

The data collected in VR training scenarios provides authentic behavioral insights, reflecting how participants are likely to respond in real-world situations 90% of the time. This is particularly evident during their initial session, when reactions are most influenced by instinctive emotional responses.

Use Cases

Exploring several use cases can demonstrate how analyzing VR training data is beneficial in understanding employee behavior and informing future organizational strategies.

Safety Scenario: Assessing Behavioral Responses

Consider a safety training scenario implemented by one of our clients, simulating an evacuation in the event of a fire or smoke. During a simulated normal workday, employees witness colleagues entering from the left and engaging in typical work activities. Suddenly, a fire alarm triggers, presenting trainees with multiple exit options. Data analysis revealed that 80% of employees defaulted to exiting through the same door they entered—the left one—or followed their colleagues. Interestingly, company emergency protocols recommend using the designated evacuation exit on the right.

This discrepancy between practiced behaviors and prescribed procedures raises an important question: Should the company retrain employees to follow the established emergency protocol, or should the protocol be adjusted to align with natural employee tendencies?

Soft Skills Scenario: Evaluating Engagement and Adaptability

Soft skills development is another area where VR training proves invaluable. Our analysis often focuses on the frequency of scenario engagement rather than the perfection of a single attempt. It's revealing that most trainees revisit scenarios multiple times, choosing various approaches each time. This pattern is significant because mastering soft skills isn't about following a strict process or protocol. Instead, it involves learning through mistakes, experimentation, and experiencing diverse outcomes. This is crucial for preparing trainees to handle real-life challenges such as resistant behavior, emotional regulation, and difficult interactions with customers or colleagues. By leveraging analytics, we can determine if trainees are adequately prepared or if further encouragement is needed to explore various outcomes and learn more effectively.

The Value of Analytics

The primary purpose of utilizing analytics in training is to facilitate continuous improvement. Training analytics serve as a crucial tool in achieving this objective.

By integrating training analytics, you gain detailed insights into the decisions made by participants during sessions. This enables a deeper understanding of their behavior, thoughts, and experiences, providing a strong foundation for meaningful discussions and feedback.

Furthermore, analytics play a vital role in enhancing the skills of VR training developers themselves. By examining this data and absorbing feedback directly from trainees, developers can consistently elevate the quality of the training programs they design. This iterative process of learning and improvement ensures that VR training remains effective and responsive to the needs of its users.

Utilizing Analytics Effectively

Once a VR training session concludes, leveraging analytics can greatly enhance the learning experience. Here’s how to do it effectively:

• Visual data display — Initiate a group discussion by presenting a visual breakdown of the most and least selected responses for each scenario on a screen. This approach helps stimulate conversation and provides a clear perspective on collective and individual decision-making trends.
• Individual discussions — Use the data to engage with specific trainees individually, guiding them through their choices and identifying areas for improvement. This personalized feedback can be instrumental in enhancing their learning experience.
• Post-scenario surveys — To gather additional insights, consider deploying a survey at the end of each scenario. This can help capture more nuanced feedback on the trainees' experiences and perceptions.
• Group-based analysis — Segment the data by specific groups, such as job roles (e.g., engineers, managers), to discern patterns in decision-making that may correlate with professional responsibilities. This analysis can pinpoint which groups may need more focused training or different types of scenarios.
• Tracking progress over time — If trainees repeat scenarios, compare their past and present results to assess improvements or changes in their decision-making. This longitudinal view can reveal the efficacy of the training and areas where it may need to evolve.
• Addressing trainee concerns — Be aware of the potential discomfort trainees might feel about being monitored during training. It's crucial to reassure them that the purpose of tracking their choices is to facilitate learning, not to penalize them. Emphasize that making mistakes within the safe environment of VR training is a positive and essential part of the learning process.
• Integrating with LMS Systems — For those using Learning Management Systems (LMS), consider integrating your VR training data with tools like Warp Studio to streamline and synchronize training outcomes, enhancing the overall management and accessibility of training data.

By implementing these strategies, you can maximize the benefits of analytics in VR training, fostering a more engaging, responsive, and effective learning environment.