Applying Data Organizational Techniques to Enhance Air Force Learning

Abstract

The USAF and the DoD use traditional schoolhouses to educate and train personnel. The physical aspects of these schoolhouses limit throughput. A method to increase throughput is to shift towards an asynchronous learning environment where students move through content at individually. This research introduces a methodology for transforming a set of unstructured documents into an organized TM students can use to orient themselves in a domain. The research identifies learning paths within the TM to create a directed KSAT. We apply this methodology in four case studies, each an education or training course. Using a graph comparison metric and the topic identification rates for the TMs, we tested a whitelisting algorithm to identify topics with up to 81 accuracy, and leveraged a standalone LDA algorithm and the same LDA algorithm with ConceptNet for topic naming. The research also produced a KSAT for all case studies and two modified KSATs. The research shows that TMs and KSATs can automatically be created with minimal user input. This methodology could help increase throughput in Air Force education and training pipelines.

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Document Details

Document Type
Technical Report
Publication Date
Mar 26, 2020
Accession Number
AD1103023

Entities

People

  • Jacob A Q Orner

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Case Studies
  • Computational Science
  • Computer Languages
  • Computer Science
  • Cybersecurity
  • Cyberspace Operations
  • Data Set
  • Department Of Defense
  • Digital Data
  • Distance Learning
  • Education
  • Information Systems
  • Instructors
  • Language
  • Model Based Systems Engineering
  • Natural Language Computing
  • Natural Language Processing
  • Natural Languages
  • Ontologies
  • Standards
  • Students
  • Systems Engineering
  • Training
  • United States

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Military Leadership and Professional Education.
  • Neural Network Machine Learning.