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Naturalistic Observations

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...

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Related Experiment Video

Updated: May 23, 2025

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

Explainable XR: Understanding User Behaviors of XR Environments Using LLM-Assisted Analytics Framework.

Yoonsang Kim, Zainab Aamir, Mithilesh Singh

IEEE Transactions on Visualization and Computer Graphics

|March 10, 2025

Summary

This summary is machine-generated.

Explainable XR is a new framework that analyzes user behavior across augmented, virtual, and mixed reality (XR) environments. It uses Large Language Models (LLMs) to provide actionable insights into user actions and interactions in immersive applications.

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Area of Science:

Human-Computer Interaction
Immersive Analytics
Extended Reality (XR)

Background:

Existing XR analytics struggle with cross-virtuality transitions, multi-user collaboration, and multimodal data.
Current frameworks lack a unified approach for analyzing diverse Extended Reality (XR) user behaviors.

Purpose of the Study:

To introduce Explainable XR, a versatile framework for analyzing user behavior in diverse XR environments.
To address limitations in current XR analytics by offering a virtuality-agnostic solution for data collection, analysis, and visualization.

Main Methods:

Developed a novel User Action Descriptor (UAD) schema for capturing multimodal user actions, intents, and contexts.
Created a platform-agnostic XR session recorder and a visual analytics interface.
Integrated Large Language Models (LLMs) for assisted data interpretation and insight generation.

Main Results:

Demonstrated Explainable XR's versatility through five use-case scenarios across individual and collaborative XR applications.
Technical evaluations and user studies confirmed high usability and the delivery of multifaceted, actionable insights.
The framework effectively handles multimodal data and cross-virtuality transitions in XR analytics.

Conclusions:

Explainable XR provides a robust and usable solution for understanding user behavior in immersive environments.
The framework facilitates deeper exploration and analysis of XR session data through LLM-assisted insights.
Explainable XR enhances the study of human-computer interaction within the evolving landscape of Extended Reality.