Employing Machine Learning to Predict Student Aviator Performance

Abstract

Machine learning analysis of student aviator training performance data offers novel and more accurate methodologies for performance assessment that includes identifying students for attrition or remediation as well as optimal pipeline assignments. Machine learning provides an opportunity to better evaluate students by fully examining every indicator of performance throughout a students training: from subtest scores on the aviation selection test battery to test scores during initial ground school through each graded item on every flight event. The goal is to reduce time-to-train, improve aviator quality, and reduce training costs from failure to complete training. In this research, we identify important predictors and develop prediction models of performance in Primary, Intermediate, and Advanced flight training based on data from Aviation Selection Test Battery (ASTB), Introductory Flight Screening (IFS), and Aviation Preflight Indoctrination (API) training.

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Document Details

Document Type
Technical Report
Publication Date
Oct 14, 2020
Accession Number
AD1118295

Entities

People

  • Magdi N. Kamel

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Big Data
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Flight Training
  • Indicators
  • Information Science
  • Machine Learning
  • Model Tests
  • Naval Aviation
  • Predictive Modeling
  • Statistics
  • Students
  • Training

Fields of Study

  • Education

Readers

  • Aviation Science / Aeronautics.
  • Neural Network Machine Learning.
  • Psychometric Testing or Psychological Assessment.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks