In recent times, we have seen exponential growth of immersive technologies: Augmented Reality, Virtual Reality, and Mixed Reality. Similarly, expansion in the field of sensor technology and the applications of artificial intelligence: virtual assistants, gesture recognition, eyeball tracking have fuelled the development of human interactivity and interaction within the virtual environment. The combination of Virtual Reality and Artificial Intelligence-powered sensors offers a new approach to Data Analytics and makes it inevitable for organizations to incorporate data-driven science in the training and development units.
Recording, Analysing and Reporting Workers Performance
One of the most important components in the performance of an industrial worker is reaction time. Reaction time or response time refers to the amount of time that takes place between when we perceive something when we respond to it. In a highly uncertain and dynamic manufacturing environment, a slight waver during operation and maintenance tasks can have a catastrophic result.
Imagine a situation, where an industrial trainee is inside a virtual oil refinery who is instigated with different fire fighting situations by his trainer. He sees a fire extinguisher nearby; pull the pin, points the extinguisher nozzle at the fire and sweep from side to side at the fire until the fire appears to be out. This provides an unprecedented opportunity to evaluate, analyze their performance and practice response behavior beforehand. The trainee’s interaction, movement, and gaze within a virtual environment can be recorded, measured and analyzed in an analytics dashboard.
Making Informed Decisions through Predictive Analysis
Data-Driven Analytics is the most important parameter in the Learning and Development program. It assists the L&D team in tracking employee’s behavior and make informed decisions. In order for Virtual Reality training and learning to be successful it’s important to integrate a back-end portal that records the trainee’s psychological and physiological data in form of graphs and charts; encapsulates insightful metrics like visual-spatial memory, heart-rate, number of trials and errors.
The aggregated insight can then be put to use to predict an event or an outcome also termed as Predictive Analysis. In this process, trainees’ experiences in VR can be segregated into different predictive models that can help organizations to provide personalized feedback and assessment to trainees. For example, a trainer can visualize the performance of his/her trainees on a data analytics dashboard. He/She can identify the learning styles of different trainees, make his/her judgment and provide iterative feedback. On the other hand, trainees can also visualize their performance and further practice in VR to enhance their skills and put them into work in real-life.
Collaborative Training By Building Cohorts
By implementing data analytics in Virtual Reality, instructors can understand trainees’ abilities and create cohorts based on attitude and aptitude. According to a report, using the closed-cohort model of program delivery can enhance professional learning and skill development because the structure provides continuity and opportunities for participants to learn and practice skills in group goal-setting. The closed-cohort structure also supports the utilization of long-term developmental activities and point-counterpoint discussions that are difficult to integrate effectively across individual courses over time.