Review article
https://doi.org/10.5552/crojfe.2024.2142
Incorporating Simulators into a Training Curriculum for Forestry Equipment Operators: A Literature Review
Erin Burk
; Northern Arizona University School of Forestry 200 E Pine Knoll Drive 86011, Flagstaff Arizona USA
*
Han-Sup Han
; Northern Arizona University Ecological Restoration Institute 200 E Pine Knoll Drive 86011, Flagstaff Arizona USA
Mathew Smidt
; US Forest Service Southern Research Station 521 Devall Drive 36849, Auburn Alabama USA
Bruce Fox
; Northern Arizona University School of Forestry 200 E Pine Knoll Drive 86011, Flagstaff Arizona USA
* Corresponding author.
Abstract
Forestry equipment simulators offer opportunities for new operators to become familiar with operating logging machines as well as a promising solution to the high costs of training forestry equipment operators. Current literature lacks a synthesis on how best to train forestry equipment operators using simulators. The goal of this review was to identify effective ways to incorporate forestry equipment simulators into an equipment operator training curriculum. We analyzed a total of 14 independent studies in which construction and forestry equipment operators were trained on simulators and engaged in discussions with nine professionals in the field of heavy equipment operator training. In this review, traditional machine training and simulator training practices are introduced. Then, four key aspects of skill acquisition for forestry equipment operators are identified. Information collected from peer-reviewed literature and discussions with industry experts are used to consider how each aspect of skill acquisition is addressed in both traditional training using real machines and simulator-based training. Drawing on these sources, benefits and drawbacks of traditional machine training and simulator-based training for forestry equipment operators are synthesized and discussed. Finally, a model for an integrated and adaptive training curriculum that incorporates principles and technologies from both traditional machine training and simulator training is presented.
Keywords
Hrčak ID:
311939
URI
Publication date:
12.1.2024.
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