Employing Machine Learning to Predict Student Aviator Performance
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Authors
Kamel, Magdi N.
Subjects
Machine learning
Data analytics
Predictive models
Aviation training
Data analytics
Predictive models
Aviation training
Advisors
Date of Issue
2020-10
Date
October 2020
Publisher
Monterey, California. Naval Postgraduate School
Language
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 student’s 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.
Type
Technical Report
Description
Series/Report No
Department
Information Sciences (IS)
Identifiers
NPS Report Number
NPS-IS-20-002
Sponsors
Naval Research Program
Funder
Navy Research
Program
Format
99 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted.