Retention and Fading of Military Skills: Literature Review

Abstract

The Canadian Forces (CF) trains in a wide variety of skills. For reasons of efficiency and economy, it is important to conduct no more refresher training than is necessary to keep performance at the desired skill level. This work surveyed the scientific and technical literature for models of skill fading and tools for determining when refresher training is required. In particular, the literature review investigated models for predicting skill retention relevant to the military domain. Subjective approaches, qualitative approaches, and quantitative have been used to model skill retention. Currently the U.S. Army Research Institute's Users' Decision Aid (UDA) model is the most advanced model. The UDA is one of few approaches for which empirical research has been done to assess its applicability and practicality. The UDA has been demonstrated to be relatively easy to administer, although some training is needed to perform the ratings correctly and reliably. The UDA itself is low-cost and requires no special equipment. The UDA, however, has received empirical validation from just one study that reports a comparison between UDA predictions and actual retention data. The literature review identifies a set of lessons learned and discusses five specific recommendations.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2000
Accession Number
ADA593268

Entities

People

  • David J. Bryant
  • Harry Angel

Organizations

  • HumanSystems Incorporated

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Applied Psychology
  • Army Personnel
  • Cognition
  • Combat Forces
  • Combat Readiness
  • Employment
  • Lessons Learned
  • Literature Surveys
  • Military Science
  • Military Training
  • National Security
  • Personnel Management
  • Psychology
  • Students
  • Task Performance And Analysis
  • Warfare

Fields of Study

  • Education

Readers

  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.