Performance curves of skill acquisition in virtual environments for basic operation of harvesters and forwarders

Abstract

The need for safe, efficient, and standardized training methods has promoted the use of virtual reality simulators as an alternative or complementary tool to conventional training, especially in high-risk operational industries such as forest operations. In this study, the improvement in performance and the acquisition of technical skills were evaluated using individual and group performance curves, with task execution time as the main indicator. Ten trainees with no experience with forestry machinery voluntarily participated in basic scenarios using a Komatsu harvester (gripping and felling) and forwarder (loading and unloading) simulator. The aim was to develop basic operational skills, promote familiarization and adaptation, and train control commands. The training consisted of ten trials per trainee distributed across six scenarios with varying levels of complexity and duration. Exponential performance models were used to describe the evolution, as they provided an adequate representation of the data, demonstrating that performance improvement based on task execution time is nonlinear. The results show a rapid and steep initial improvement followed by a phase of deceleration and progressive stabilization. Scenarios with greater complexity or longer execution times exhibited slower stabilization and higher inter-individual variability, suggesting increased cognitive and psychomotor demands, while scenarios with shorter execution times showed faster adaptation with less dispersion. All six scenarios showed a consistent pattern of performance improvement, with a progressive decrease in execution time across trials. These findings support the integration of virtual reality technologies (especially simulators) into training programs and highlight the use of performance curves as a tool to evaluate improvements during training.

Keywords

Virtual reality / Simulator / Performance curves / Training / Forestry operations

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Funding

This research was supported by a PNNR program - Transformare digitală pentru inovare şi competitivitate funded through UE’s NextGenerationEU

European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022)

COST ACTION - CA23135 - Bringing Digital Data and Reality Together - Augmented Reality in Forestry (ARiF) - WG 1, WG 2, WG5.

PNNR program - Transformare digitală pentru inovare şi competitivitate funded through UE’s NextGenerationEU

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