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Related Experiment Video

Updated: Feb 15, 2026

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Published on: March 27, 2013

Tracing the learning path: Revealing skill acquisition phases in a virtual environment through behavioral tracking.

Anaïs Raison¹, Nathalie Le Bigot¹, Olivier Augereau²

¹Univ Brest, Lab-STICC, CNRS, UMR 6285, F-29200, Brest, France.

Acta Psychologica

|February 13, 2026

Summary

This study introduces a new method to identify learning phases using virtual environments. It tracks user actions to personalize training and improve skill acquisition.

Keywords:

Human performance Learning phases Mental workload Skill acquisition Virtual environment

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Area of Science:

Cognitive Science
Human-Computer Interaction
Virtual Reality Training

Background:

Skill acquisition is often lengthy and resource-intensive.
Virtual environments offer a viable alternative for in-situ training and data collection.
Personalized training requires understanding individual learning needs and stages.

Purpose of the Study:

To identify distinct phases of skill acquisition.
To develop a behavioral method for real-time learning phase detection.
To align identified phases with established cognitive theories like ACT-R.

Main Methods:

Sixty-three participants practiced an assembly task in a virtual environment over two sessions.
Behavioral data, including instruction consultations and errors, were logged.
Mental workload was assessed using the NASA-TLX scale.

Main Results:

Progressive skill acquisition was observed, with evidence of long-term memory retrieval.
A behavioral method effectively identified distinct learning phases.
The identified phases correlated with behavioral and cognitive patterns from ACT-R theory.

Conclusions:

The proposed method offers a non-intrusive, real-time approach to identify learning phases.
This facilitates the development of adaptive virtual training environments.
Future validation could include physiological measures and dual-task paradigms.