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  4. Learning Curves of Harvester Operators in a Simulator Environment
 
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Learning Curves of Harvester Operators in a Simulator Environment

Type
Journal article
Language
English
Date issued
2024
Author
Polowy, Krzysztof 
Rutkowski, Dariusz
Faculty
Wydział Leśny i Technologii Drewna
Journal
Forests
ISSN
1999-4907
DOI
10.3390/f15081277
Web address
https://www.mdpi.com/1999-4907/15/8/1277
Volume
15
Number
8
Pages from-to
art. 1277
Abstract (EN)
Simulator training helps provide safe and cost-effective training for operators of modern forestry machines that require high motor skills, constant concentration, and proper planning. The aim of the study was to analyze the learning curves of the trainees in order to determine the period during which most development takes place. In this study, 11 trainees were trained on a John Deere harvester simulator for approximately 15 h each. In each case, a clear learning curve could be identified, despite high inter- and intra-person variability. Effective time showed a steady decrease during training, with a group minimum at the end of training (1.25 min). Crane tip distance per tree dropped rapidly in the first 3–4 h, followed by a more gradual decrease to reach a minimum of 23.8 m. Crane control showed a significant increase from an initial 0.63 to a maximum of 0.8 by the 9th hour of training. A number of crane functions used simultaneously increased more rapidly to almost a maximum value (1.8) already in the 5th hour. The individual curves for each trainee were highly variable, showing a wide range of values and shapes. In conclusion, most personal development occurs during the first phase of simulator training, which typically takes approximately 9–10 h. It is important to consider significant inter-personal variability and tailor the duration of simulator training to individual needs.
Keywords (EN)
  • vocational training

  • skills development

  • forest operations

  • forest utilization

  • forestry crane

License
cc-bycc-by CC-BY - Attribution
Open access date
July 23, 2024
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