Identifying Early Predictors of Learning in VR-based Drone Training

Author:

Hayes John1,Dwivedi Shivangi1,Karthikeyan Rohith2,Abujelala Maher1,Kang John1,Ye Yang3,Du Eric3,Mehta Ranjana K.1

Affiliation:

1. Department of Industrial and Systems Engineering, Texas A&M University

2. Department of Mechanical Engineering, Texas A&M University

3. Department of Civil & Coastal Engineering, University of Florida

Abstract

The use of small unmanned aerial systems, or drones, has grown rapidly in recent years, but the FAA has no formal requirement for hands-on training. Physical drone training has major limitations, and virtual reality (VR) offers a promising alternative. This study sought to identify early performance markers of learning in VR-based drone training using a physical controller. 14 participants completed a customized VR-based drone training curriculum, while performance metrics, perceptions of workload, and physiological data were collected. Participants were clustered into high and low performers based on a final evaluation task, and separate analyses of variance were conducted to test performance differences between the two groups over time in each training level. We found significant differences in the performance metrics and subjective workload of low and high performers throughout training, suggesting that performance can be predicted early in training and opening the door to future adaptive training systems.

Publisher

SAGE Publications

Subject

General Medicine,General Chemistry

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1. Building User Proficiency in Piloting Small Unmanned Aerial Vehicles (sUAV);2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Multi-Objective Reinforcement Learning for Autonomous Drone Navigation in Urban Area;Construction Research Congress 2024;2024-03-18

3. Collaborative Virtual Training with Embodied Physics and Haptic Feedback: Construction Manual Material Handling as an Example;Computing in Civil Engineering 2023;2024-01-25

4. Adaptive Training on Basic AR Interactions: Bi-Variate Metrics and Neuroergonomic Evaluation Paradigms;International Journal of Human–Computer Interaction;2023-09-01

5. Spatial Memory of BIM and Virtual Reality: Mental Mapping Study;Journal of Construction Engineering and Management;2023-07

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