Automated Assessment and Adaptive Multimodal Formative Feedback Improves Psychomotor Skills Training Outcomes in Quadrotor Teleoperation

TL;DR

This paper presents an automated, adaptive multimodal feedback system for quadrotor teleoperation training that enhances safety and learning outcomes by providing personalized formative feedback based on skill assessment.

Contribution

It introduces a novel system that automatically assesses skills and delivers adaptive multimodal feedback, improving training effectiveness for complex psychomotor tasks.

Findings

Participants perceived feedback positively.

Multimodal feedback increased safe landings.

Adaptive feedback improved learning outcomes.

Abstract

The workforce will need to continually upskill in order to meet the evolving demands of industry, especially working with robotic and autonomous systems. Current training methods are not scalable and do not adapt to the skills that learners already possess. In this work, we develop a system that automatically assesses learner skill in a quadrotor teleoperation task using temporal logic task specifications. This assessment is used to generate multimodal feedback based on the principles of effective formative feedback. Participants perceived the feedback positively. Those receiving formative feedback viewed the feedback as more actionable compared to receiving summary statistics. Participants in the multimodal feedback condition were more likely to achieve a safe landing and increased their safe landings more over the experiment compared to other feedback conditions. Finally, we identify themes to improve adaptive feedback and discuss and how training for complex psychomotor tasks can be integrated with learning theories.