Measurement of Visitor Behavioral Engagement in Heritage Informal Learning Environments Using Head-Mounted Displays

Area of Science:

• Human-Computer Interaction
• Museum Studies
• Educational Technology

Background:

• Measuring visitor engagement in informal learning environments like museums is challenging.
• Existing research often overlooks multidimensional engagement analysis in complex settings.
• Systematic analysis of visitor engagement for spatial and educational optimization is underdeveloped.

Purpose of the Study:

• To introduce an integrated head-mounted display (HMD)-based framework for measuring visitor engagement.
• To combine meso-scale spatial analysis with micro-level temporal engagement modeling.
• To establish methodological foundations for intelligent heritage environments.

Main Methods:

• Developed an HMD-based framework integrating behavioral engagement heatmaps (meso-scale) with eye-tracking and headset pose data (micro-level).
• Utilized an edge-optimized long short-term memory (LSTM) model for real-time engagement measurement.
• Conducted a two-stage user study (N=20 for feature analysis, N=15 for modeling) with physiologically grounded features.

Main Results:

• Achieved real-time engagement measurement with a mean squared error (MSE) of 0.145.
• Synthesized planar trajectory heatmaps and panoramic fixation distributions for comprehensive analysis.
• Demonstrated HMDs as effective precision measurement tools for visitor engagement.

Conclusions:

• The HMD-based framework offers a novel approach to understanding visitor engagement in informal learning environments.
• Integrated meso-micro analytics enable both real-time adaptive support and post-hoc exhibition design insights.
• This methodology lays the groundwork for intelligent heritage environments that dynamically respond to visitor engagement.