Informing the Long-Term Learner Model: Motivating the Adult Learner (Phase 1)

Abstract

The United States (US) Army has valued training across a wide range of performance areas and has used a variety of platforms to instantiate the training seen as the best fit for the knowledge, skills, and abilities required for mission success. As the United States is coming out of wartime, an opportunity is available to consider the effectiveness and cost of training programs and platforms. The Army is developing a framework for providing personalized, on-demand, computer-based instruction under the Generalized Intelligent Framework for Tutoring (GIFT) program. Of particular interest for this effort is understanding the intersection of personality factors, motivation, and reinforcers that are relevant to informing the Long-Term Learner Model. The approach proposed is to use existing data as a starting point for identifying important correlates of the aforementioned concepts and evaluating their application within a learning/training environment. The goal is to develop a motivator capability that can be incorporated into GIFT. Phase 1 focused on the initial development of a Motivator Assessment Tool (MAT) and integration of the MAT in GIFT for purposes of validation. The focus for Phases 2 and 3 will be to refine the MAT based upon validation experiments and implement that version as an adapting strategy in GIFT for learning in a determined task. The expectation is that targeted motivator adaption strategy will increase the rate and retention of learning.

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Document Details

Document Type
Technical Report
Publication Date
Sep 28, 2017
Accession Number
AD1041013

Entities

People

  • Elizabeth Biddle
  • Elizabeth Lameier
  • Lauren Reinerman-jones
  • Michael W. Boyce

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Applied Psychology
  • Brain
  • Cognition
  • Cognitive Science
  • Cognitive Workload
  • Computer Programming
  • Computers
  • Health Services
  • Human Behavior
  • Medical Personnel
  • Motivation
  • Neurosciences
  • Personality
  • Psychology
  • Social Psychology
  • Students
  • United States

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  • Computational Modeling and Simulation
  • Economics
  • STEM Education