An Army Experiential Learning Model Job Aid

Abstract

The purpose of this work was to create a tool to help instructors at the Maneuver Captains Career Course (MCCC) bridge the operator-to-educator transition (Swaim, 2017) by supporting their understanding of the Army Experiential Learning Model (ELM; see The Army University, Adult Teaching and Learning Users Guide). The ELM Job Aid was developed in a collaboration between the U.S. Army Research Institute (ARI) and the MCCC. For each element of the ELM, the job aid includes an overview of the purpose, a summary of typical methods used by MCCC instructors, alternative approaches, and Tips and Tricks based on the practices of seasoned MCCC instructors. Notably, the tool is intended to be used in conjunction with experiences gained in the Common Faculty Development-Instructor Course (CFD-IC) and in a program of instruction (POI)-specific certification course that serves to help apprentice instructors develop pedagogical content knowledge. The ELM Job Aid presented here addresses a way to further support content-specific teaching when faced with the time and resource constraints that are characteristic of military training and education settings.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2022
Accession Number
AD1167517

Entities

People

  • Ashley H. Wittig
  • Camilla C. Knott
  • Frederick J. Diedrich
  • Kerri C. Chik
  • Scott T. Geers

Organizations

  • U.S. Army Research Institute for the Behavioral and Social Sciences

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Apprenticeship
  • Army Training
  • Best Practices
  • Computational Fluid Dynamics
  • Contracts
  • Doctrine
  • Education
  • Instructors
  • Learning
  • Military Education
  • Military Research
  • Military Training
  • Schools
  • Social Sciences
  • Students
  • Training
  • Universities

Fields of Study

  • Education

Readers

  • Military Training and Readiness Simulation
  • Neural Network Machine Learning.
  • Occupational Health and Safety.