The University of Southampton
University of Southampton Institutional Repository

A new approach for Training Needs Analysis: A case study using an Automated Vehicle

A new approach for Training Needs Analysis: A case study using an Automated Vehicle
A new approach for Training Needs Analysis: A case study using an Automated Vehicle
Considerable resources are invested each year into training to ensure trainees have the required competencies to safely and effectively perform their tasks/jobs. As such, it is important to develop effective training programmes which target those required competencies. One method that can be used at the start of the training lifecycle to establish the tasks and competencies that are required for a task/job and is considered an important activity to perform when developing a training programme is a Training Needs Analysis (TNA). This article presents a new TNA approach and uses an Automated Vehicle (AV) case study to demonstrate this new approach for a specific AV scenario within the current UK road system. A Hierarchical Task Analysis (HTA) was performed in order to identify the overall goal and tasks that drivers need to perform to operate the AV system safely on the road. This HTA identified 7 main tasks which were decomposed into 26 sub-tasks and 2428 operations. Then, six AV driver training themes from the literature were combined with the Knowledge, Skills and Attitudes (KSA) taxonomy to identify the KSAs that drivers need to perform the tasks, sub-tasks and operations that were identified in the HTA (training needs). This resulted in the identification of over 100 different training needs. This new approach helped to identify more tasks, operations and training needs than previous TNAs which applied the KSA taxonomy alone. As such, a more comprehensive TNA for drivers of the AV system was produced. This can be more easily translated into the development and evaluation of future training programmes for drivers of AV systems.
Automated Vehicles, Driver training, Hierarchical Task Analysis, Training Needs Analysis
0003-6870
Merriman, Siobhan
93bd85cd-f5a1-4b2c-96f5-7f1df776d07a
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd
Merriman, Siobhan
93bd85cd-f5a1-4b2c-96f5-7f1df776d07a
Plant, Katherine
3638555a-f2ca-4539-962c-422686518a78
Revell, Kirsten
e80fedfc-3022-45b5-bcea-5a19d5d28ea0
Stanton, Neville
351a44ab-09a0-422a-a738-01df1fe0fadd

Merriman, Siobhan, Plant, Katherine, Revell, Kirsten and Stanton, Neville (2023) A new approach for Training Needs Analysis: A case study using an Automated Vehicle. Applied Ergonomics, 111, [104014]. (doi:10.1016/j.apergo.2023.104014).

Record type: Article

Abstract

Considerable resources are invested each year into training to ensure trainees have the required competencies to safely and effectively perform their tasks/jobs. As such, it is important to develop effective training programmes which target those required competencies. One method that can be used at the start of the training lifecycle to establish the tasks and competencies that are required for a task/job and is considered an important activity to perform when developing a training programme is a Training Needs Analysis (TNA). This article presents a new TNA approach and uses an Automated Vehicle (AV) case study to demonstrate this new approach for a specific AV scenario within the current UK road system. A Hierarchical Task Analysis (HTA) was performed in order to identify the overall goal and tasks that drivers need to perform to operate the AV system safely on the road. This HTA identified 7 main tasks which were decomposed into 26 sub-tasks and 2428 operations. Then, six AV driver training themes from the literature were combined with the Knowledge, Skills and Attitudes (KSA) taxonomy to identify the KSAs that drivers need to perform the tasks, sub-tasks and operations that were identified in the HTA (training needs). This resulted in the identification of over 100 different training needs. This new approach helped to identify more tasks, operations and training needs than previous TNAs which applied the KSA taxonomy alone. As such, a more comprehensive TNA for drivers of the AV system was produced. This can be more easily translated into the development and evaluation of future training programmes for drivers of AV systems.

Text
A new Approach for Training Needs Analysis AE pure - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (3MB)

More information

Accepted/In Press date: 3 March 2023
e-pub ahead of print date: 19 April 2023
Published date: 1 September 2023
Additional Information: Funding Information: This research was funded by IAM RoadSmart and the Engineering and Physical Sciences Research Council . These funders had no involvement in the study design, in the collection, analysis, and interpretation of the data, in the writing of the report, and in the decision to submit this paper for publication. Publisher Copyright: © 2023 The Authors
Keywords: Automated Vehicles, Driver training, Hierarchical Task Analysis, Training Needs Analysis

Identifiers

Local EPrints ID: 476477
URI: http://eprints.soton.ac.uk/id/eprint/476477
ISSN: 0003-6870
PURE UUID: 57b98185-1791-4a12-bc50-3eaab46063a9
ORCID for Siobhan Merriman: ORCID iD orcid.org/0000-0002-0519-687X
ORCID for Katherine Plant: ORCID iD orcid.org/0000-0002-4532-2818
ORCID for Neville Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 03 May 2023 17:06
Last modified: 17 Mar 2024 04:16

Export record

Altmetrics

Contributors

Author: Siobhan Merriman ORCID iD
Author: Katherine Plant ORCID iD
Author: Kirsten Revell
Author: Neville Stanton ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×