Tag

Analytics

What are the Internet of Behaviour (IoB) implications for XR Analytics?

CORTEXR dives into the emerging trend of the Internet of Behaviours and its impact on XR development

The Internet of Behaviour (IoB) is a technology trend with significant opportunities and benefits for extended reality (XR). It adds a layer of psychology to the Internet of Things (IoT) to interpret ‘digital dust’ linked to behavioural patterns.

The ultimate goal is to connect devices, analyse user behaviour, and influence future behaviours.

Gartner predicted in 2021 that 40 percent of the world’s population will face at least one IoB programme in 2025 across verticals such as from marketing, healthcare, and manufacturing.

Here’s a deep dive into the IoB implications for virtual, augmented, and mixed reality (VR/AR/MR) as well as the Metaverse in the mission to learn how humans behave in 3D experiences with six degrees of freedom (6DoF).

What is the Internet of Behaviour (IoB)?

IoB is a protocol that uses sensors to collect behavioural data from devices. In this case, it leverages VR and AR devices such as mobile and head-mounted displays (HMDs). Data is processed using cloud computing, artificial intelligence (AI), machine learning (ML), and data science to extrapolate human behaviour.

It represents the next level of business intelligence (BI) to understand and optimise behaviour. It is, therefore, a critical component of an interoperable, synchronous and persistent Metaverse with shared experiences.

Over time, predictive analytics on human behaviour in XR will drastically improve user experiences and business performance metrics.

How Does the Internet of Behaviour Work?

IoB connects AR and VR devices to cloud services like Amazon Web Services (AWS). Application programme interfaces (APIs) collect sensor data, including gyroscope, accelerometer, and other sensors, in real-time data exchanges.

Companies store data in databases and analyse them with AI algorithms to create an intelligence engine capable of understanding user behaviour patterns. 6DoF spatial data on head, hand, and body movements in 3D space is a complex dataset. However, cognitive science and significant research and development (R&D) interpret XYZ coordinates as insights which help XR businesses understand their audiences.

Our approach to the IoB tech stack is an end-to-end data analytics platform which does all the heavy lifting so customers, regardless of data analytics expertise, can quickly see data visualisations and measure performance goals.

What Are the Benefits of IoB for the XR Industry?

Industry research shows that 83 percent of XR companies think data analytics are important to growing business, but studies also highlight a measurement gap.

This is partly because AR/VR experiences generate unique and unstructured user behaviours, compared to the ‘Internet of clicks, taps and likes,’ as well as a reliance on legacy toolkits like web analytics, surveys, and others.

IoB is a new approach specifically designed to:

collect real-time 6DoF data from platforms and devices
quickly analyse behaviours in 3D experiences to improve performance and productivity, and
standardise XR metrics to make predictions about future actions based on past behaviours.

From navigation, or the position, pathway, priority and order of events, to attention involving the time, sequence and priority of content viewed, there are benefits for all extended reality use cases.

How do you apply IoB to your XR business?

A low and no-code plug-in for 3D engines and XR platforms collects real-time sensor data from handsets and headsets. CORTEXR’s intelligence engine analyses 6DoF spatial data in the cloud using AI and cognitive science.

Prebuilt dashboards present performance metrics and data visualisations across all your AR, VR and Metaverse projects. Our Unity plug-in is now live so users can easily add IoB to projects.

The IoB is a new framework to better understand your users, identify behaviours which aren’t necessarily obvious, and improve business performance. It’s a great diagnostic tool and is also proven to be predictive of behaviour.

What Are the Opportunities for IoB and XR?

Whilst other sectors have been applying IoB to digital marketing, IoT, location services, and other key focuses over the next few years, the opportunity for the XR industry is still nascent.

CORTEXR launched it solution to address this knowledge gap in order to understand human behaviour in XR through the lens of psychology and cognitive science. The solution covers verticals such as healthcare, entertainment, education, and training, where customers can tap into a wealth of spatial data.

Users can learn key analytics in use cases such as determining how health conditions like dementia correlate with spatial diagnostics or how eCommerce virtual try-ons correspond with purchase intent. People can also gain clear insights into how training module behaviours correspond with learning attainment.

What are the data privacy concerns of IoB?

Data privacy is, quite rightly, a crucial debate for the broader tech industry. People are familiar with synchronising their devices and granting permissions. However, biometric fingerprints of faces and eyes are particularly intrusive, and tech company practices in digital marketing require government regulation from frameworks such as the European Union’s General Data Protection Regulations (GDPR).

XR and IoB are, therefore, vulnerable to ethical questions about how data is collected and used at a scale. CORTEXR has a policy of deliberately avoiding Personally Identifiable Information (PII).

Raw data on 6DoF behaviour doesn’t collect PII — basic device IDs only measure user numbers and restricts GPS data to guarantee anonymity. Users do not want or need PII data to deliver our XR data analytics services.

Quelle:

What are the Internet of Behaviour (IoB) implications for XR Analytics?

14. April 2023 0 comment

Knowledge Research

Harena Data introduces its new ‘COGNISAT’ cognitive Virtual Reality analytics platform

Harena Data, an esports community aggregator, has recently revealed its new cognitive analytics platform ‘COGNISAT’. The platform combines sport specific virtual reality- (VR) based analytics with video game metrics from GYO Score (an esports and gaming data analytics platform) and computer-based soft skill cognitive metrics and performance profiling into a common data set.

According to the company, the data set provides a comprehensive cognitive view of a traditional or virtual athlete and helps athletes, recruiters and coaches to better understand the athlete today, provides tools to improve cognitive performance using the virtual world and the real world, provides insights into team management, and identifies cognitive areas most damaged by injury or setback for athletes in rehabilitation.

“COGNISAT is Moneyball on steroids for all sports, virtual or traditional, at an extremely affordable price point, that will become more and more powerful the more athletes use it. It’s an advanced analytic and learning machine that analyzes athletes and empowers them to become better using the best systems available in both traditional and virtual worlds,” said Derek Pew, CEO at Harena Data. “We envision COGNISAT replacing many traditional assessment tools used in youth development, college recruitment and employment recruitment of scholar athletes.”

Harena Data stated that COGNISAT is now available to license to colleges, universities, professional sports teams, esports teams, youth development organizations, sports leagues, and other interested parties.

The company noted the following required and recommended hardware specifications for its COGNISAT platform:

Required:

HTC VIVE
Tracked motion controllers
Room Scale

Recommended specs:

OS: Windows 10 (64bit)
Processor: Intel Core i7 Skylake or equivalent
Memory: 16 GB RAM
Graphics: Nvidia GTX 1070 or equivalent
DirectX: Version 12
Storage: 1 GB available space

Harena Data has developed GYO Score to be a data analytics, league development, and player management tool for the esports industry. In addition to GYO Score, Harena Data specializes in esports consultation regarding the development and deployment of esports venues, scholastic esports programs, and esports league concepts. For more information on the company, please click here.

Quelle:

https://www.auganix.org/harena-data-introduces-its-new-cognisat-cognitive-virtual-reality-analytics-platform/

Image credit: Harena Data

15. Juli 2021 0 comment

Knowledge Research

VR Training Analytics: A New World Of User Data Tied To Performance Outcomes

Voice, body, and biometric feedback tracking. It’s an exciting time for VR training analytics. Find out how VR enables adaptive learning, continuous improvement, and evidence-based learning efficacy for innovative L&D professionals.

VR Equals To New Possibilities For Learning Analytics

Adaptive learning. Continuous improvement. Evidence-based learning efficacy. The rise of learning analytics is driving change in Learning and Development (L&D). If this area of focus gets you excited, you’re not alone, and VR training analytics is about to open up a whole new world for you.

“Learning analytics” jumped from third to first position in Donald Taylor’s annual L&D Global Sentiment Survey, which asks, “What will be hot in workplace learning in 2020?”[1] Yet despite the drive to collect and use data to adapt, improve, and assess the value of learning, it has been challenging for L&D to accomplish. An annual report by Watershed suggests that while 90% of L&D professionals surveyed want to measure the business impact of learning programs, 53% of L&D departments have either no or basic measurement [2].

Compared to eLearning, VR brings unparalleled access to user data because of the innate tracking capabilities inside the headset, which can then be exported to an analytics tool or dashboard. Analysis of VR data can demonstrate training effectiveness and show areas of improvement, both on an individual level and aggregated across entire learning audiences.

Combined with a strategic approach to learning evaluation outside the headset, we have a powerful formula for tying data to performance outcomes.

The Many Ways To Use VR Training Data

An important point to make is that VR user data is not only useful for analyzing after the fact. With sophisticated VR programming, we can collect data during the learning experience and adapt the experience in real time. New developments in VR include tracking body movements, facial expressions, voice recognition (speech) and vocal tone, and even biofeedback, such as heart rate.

What Can Be Tracked In VR?

Progress, Completions, And Replays

Measure and report on how many tries it took to finish a VR learning experience, and if/how many times the learner replayed the experience. This, combined with other data, can indicate whether you have areas for improvement in your learning experience.

Decision-Making

Continuous monitoring of data means you can gather data on key decision points, which can include anything from multiple-choice assessments to choices in a branching scenario. Analyzing this data can help you determine if you’ve made the questions or scenario choices too challenging or not challenging enough. It could also help you analyze patterns in an individual’s thought process or decision-making. With a branching scenario in which the paths are based on emotional response (empathetic, severe, professional), the data will show which path an employee tends to take.

Voice Recognition And Analysis

Track the verbal interactions learners have with avatars and interactive components within the VR experience to gauge the emotion and energy level demonstrated by a learner’s verbal response. Pace can be determined by the words spoken per minute. Data can reveal the tone and overall sentiment of the speaker. You can even track how many “um’s” or “likes” a person exhibits during verbal communication.

Body Position And Movement

Evaluate this data to assess if the proper physical techniques being instructed are being achieved—ideal for safety training. For soft skills training, this data can be monitored to evaluate the learner’s communication, interactions, and behavior exhibited through body language in the program. Monitor how and where learners move around a virtual environment to evaluate if you’ve created an intuitive path for them.

Retinal Eye Tracking

Data showing where learners are looking in the VR experience (and at whom they’re looking) can be visualized in a “heat map” for any point in the program. Evaluate this data to ensure key content is successfully being seen at the right moment in the learning journey. This data could also be used as key metrics for diversity and inclusion training, potentially indicating areas of unconscious bias.

Biometric Feedback

An exciting new development in VR, biometric feedback, may include monitoring a learner’s heart rate, energy expenditure, and even mapping brain activity (which is still in the early stage of development). This can provide insight into stress levels that learners feel at specific moments in the program. You can measure learners’ heart rates to determine if they’re exerting a specific amount of energy at a particular time.

Tracking Of Multimodal Data

You needn’t be limited by only VR learning data. Not only can your analytics tool monitor each VR program, but you can also aggregate data from other learning modalities, such as Augmented Reality and Mixed Reality. This gives L&D the flexibility to implement different learning modalities (for optimal learning potential) while monitoring learning activities from a single, customizable dashboard.

LMS Vs. VR Analytics Dashboard

Achieving the best integration with the LMS is a continuing journey in VR for learning. As self-contained, stand-alone applications, VR is typically launched via the VR hardware and software, rather than an LMS. However, if we want to go deeper and review and analyze more of the user data, then we need another tool: Enter the VR analytics dashboard.

The VR analytics dashboard can provide L&D professionals with these features:

Track users, completion of experiences, and assessments
Track the amount of time spent on a given situation
Count repetitions and/or interactions in the experience
Capture specific data for each unique user
Support a dashboard view with a familiar LMS look, feel, and functionality
Be customizable to provide insight into the most important data: you can add a data tracking element to just about anything in the Virtual Reality experience

Tying It All Together And Measuring Impact

To create the most effective evaluation strategy, you’ll want to combine data captured from VR learning with data gathered after the learning experience. This evaluation strategy should be tailored to your learning objectives and audience as well as business constraints.

If you’re new to training evaluation, good places to start are the Kirkpatrick Model of training evaluation and Will Thalheimer’s Learning-Transfer Evaluation Model [3] [4].

A comprehensive evaluation strategy might include surveys, observation, and data analysis to address the following areas (informed by the two models mentioned above):

Engagement
How do learners rate the level of engagement? This can be combined with user data captured inside the headset on progress and replays, and, in the near future, biometric feedback as well.
Confidence
How confident did they feel about performing the task(s) on the job before the training, and how do they feel after the training?
Relevance
How relevant did they find the training? How often do they predict they will use what they learned on the job?
Motivation
How motivated are they to apply what they learned back on the job?
Knowledge
This refers specifically to the recall of facts and information, and it is typically measured via multiple-choice assessments. This could be combined with user data on decision-making, such as with branching scenarios captured inside the headset.
Competence/Transfer
Back on the job, is the learner making decisions and performing tasks by applying what they learned—in other words, displaying behavior change? This can be measured via manager observation and/or more sophisticated assessments (possibly in VR!).
Results
What is the effect of the transfer on the business or organization? This typically requires more in-depth data analysis on Key Performance Indicators before and after the learning intervention. Given the variables involved in these metrics (Is the increase in sales due to learning? New products? Discounts?), understanding impact at this level is challenging—the holy grail of learning analytics!

Learning Analytics And The Future of L&D

Data collected both inside and outside the headset create a comprehensive VR training analytics strategy, beyond what can be done in other delivery modalities. As we move forward through precautions around physical distancing, analytics is more important than ever for capturing every interaction and reaction your learners have. Armed with the granular data to track learning, instructors can support learners with better and more personalized feedback—without having to be in the same room.

Interested in discovering more about learning analytics in VR training and seeing examples of analytics in action? Download Your Guide To VR Training Programs: Virtual Reality For Our New Reality. In this comprehensive eBook, you’ll learn the steps to successfully building a VR training program within your organization and read the case studies that prove its effectiveness. Join the webinar VR Training Program Case Study to learn all you need to know through a success story!

References:

[1] THE L&D GLOBAL SENTIMENT SURVEY 2020 – FIRST THOUGHTS

[2] Measuring the Business Impact of Learning: 2020 Survey Results

[3] The Kirkpatrick Model

[4] The Learning-Transfer Evaluation Model (LTEM)

Quelle:

Foto: Katherina Canales/SweetRush Inc.

https://elearningindustry.com/vr-training-analytics-user-data-to-measure-performance-outcomes

31. Juli 2020 0 comment

Knowledge Research

The Importance of Analytics to VR Training

VR Training is generally regarded as an incedibly impactful form of employee training. It’s immersive, memorable, interactive, and can transport you to situations you simply can’t repeat easily in real life. Whether you’re training new employees or long-tenured ones — there’s so many ways to incorporate VR into your learning & development curriculum.

One question we’ve started to get asked in the last year: How do I know if my VR Training is working? How do I see actual user data?

Obviously, you’ll know if your VR Training is working if your real-life metrics improve. This could be less workplace accidents, more output per hour, better scores on post VR experience tests.

Did you also know you can view employee VR performance directly in your InstaVR console also? We have a wealth of data showing you all about your apps’ usage, and with a little savvy approach to building Navigation Links + Hotspots, you can see exactly what’s happening within your VR apps in real-time.

To underscore this point, we wanted to cover the three most important analytics approaches you can use within InstaVR. As always, if you have any questions, let our sales team know. They’d be happy to discuss how to maximize your use of InstaVR!

1. Real-time Data on All App Interactions

Assuming the VR device used by your employees is connected to the Internet, the Reports section of InstaVR will show you everything a user does in your VR training app. This is very akin to clickstream data you can get from web page interactions — it gives you an understanding of how users are navigating and interacting with the app.

It starts with the optional Branding add-on screen called “User ID”. This allows you to easily identify which employee is using the app. We generally suggest having them input their name or a unique identifying ID number prior to launching the VR experience.

Watching this “clickstream” data gives you some valuable insights: What navigational or decision choices did the employee make? Did they finish the VR experience? How long did it take them to complete? Etc.

All of which is exportable to a CSV file, for more easily manipulating the data.

Of course, just having the data isn’t enough. You’ll also want to set benchmarks. How long should it take an employee to finish the VR experience? What is the optimal path for them to take?

If you iterate your app, or have employees re-do the same VR experience again, you can see if there are improvements over time. This is important since the main focus of VR Training is improvement.

2. User Identification of Objects Within Scenes

A great new feature for InstaVR Pro users is Polygon Hotspots. What are these? They allow a user to custom design the shape of a Hotspot within a scene. You’re also able to customize the color of the polygon.

Using Hotspots to have training app users identify things in a scene is particularly valuable. Besides polygons, you can also upload transparent .png files as icons, meaning the user can’t obviously notice the Hotspots in a scene.

You can ask a user — via text, image or audio .mp3 file — to do an action in VR. For example, you can add an audio file asking the user to: “Identify three major fire dangers in this scene, in order from most dangerous to least dangerous.”

In the Reports section of InstaVR, you are then able to see:

1. Did they identify the three correct fire dangers?
2. Did they do them in the correct order?
3. How quickly did they identify them?

You can then stack rank the employees — or even use this assessment as part of your hiring process, as Walmart does.

There’s a lot of value in making your VR Training interactive, and that value is only accentuated when you know you can see the individual results of users.

3. Answering Questions Within the VR App

Traditionally, you’ve had to ask quiz questions of VR users after they’ve taken off the their headset.

But with the flexibility of InstaVR to add Navigation Links and Hotspots — you can add questions, and then see the answers given in real-time in your Console. The answer could come in the form of answering a Multiple Choice question set up using Hotspots and Custom Icons. It could be a simple “Yes/No” question. Or it could be a question where the answer takes the user to a new scene.

A great example of this — though not for a training app — is the World Surfing League + Jeep app created using InstaVR titled “Jeep Sessions: A Surfing Journey in 360.” In that VR experience, you get to choose between a couple different driving and surfing adventures hosted by Jordy Smith and Malia Manuel. The act of which surfing adventure you choose takes you to a new scene, which gets recorded in your InstaVR Console.

You can create an elaborate, multi-scene training application where the user basically answers a series of questions that determine where they go next.

By looking at the recorded data, you can see how employees react in the scenario. If they are continually choosing the wrong option, you can spend more time either real-world or virtually training them to make the correct decisions. And we know that VR decisions are much closer to real decisions than asking hypothetical questions on paper, so you get more true-to-life answers from your VR Training users.

4. Conclusion

Making a great VR Training app is incredibly valuable. But collecting data on user behavior, iterating the training, and improving employee actions through virtual training is the ultimate goal. All of this is possible through the data being collected within InstaVR.

Now you can report back to your VP of Human Resources or Head of Talent on what specifically the VR Training achieved, and where employees need improvement.

We’ve taken the leap from having to guess what’s happening in the VR scenarios, to fully understanding all user actions. We live in a data-driven world, and the Analytics feature of InstaVR gives you unparalleled access to understanding your employee VR Training behavior. You have no excuse now — the tools are right in front of you to improve both your VR Training and your employees.

Quelle:

http://www.instavr.co/articles/general/the-importance-of-analytics-to-vr-training

7. August 2019 0 comment