Machine Learning for Analysis of Naval Aviator Training
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Authors
Rowe, Neil C.
Das, Arijit
Subjects
training
pilots
aviators
performance
testing
prediction
database
classroom
regression
correlation
classes
pilots
aviators
performance
testing
prediction
database
classroom
regression
correlation
classes
Advisors
Date of Issue
2020-10
Date
October 2020
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
This project investigated patterns in the training data of Navy aviators in an attempt to predict their success in training. With the help of the sponsor, we assembled a database from many sources of training data. This database covered 18,596 pilot and Naval Flight Officer candidates through their pretesting, classroom instruction, candidate training in generic aircraft, and candidate training in specialized aircraft. This data was a challenge to organize because it had incompatible formats and missing data. After standardizing the formats and fixing errors in the data, and aggregating sparse records to a smaller set of average scores, we had 301 features for the candidates. We then correlated their features using both numeric-correlation and nonnumeric-association (class-characterization) methods. We identified 38 kinds of measures of success in the program and particularly focused on correlations involving those. We did confirm some early indicators of success and failure in the program, but most were not surprising. We conclude that the Navy is doing a good job of identifying candidates likely to be successful.
Type
Technical Report
Description
Approved for public release; distribution is unlimited.
Series/Report No
Department
Computer Science (CS)
Identifiers
NPS Report Number
NPS-CS-20-002
Sponsors
U.S. Fleet Forces Command
Funder
NPS-20-N309-B-NRP
Format
34 p.
Citation
Distribution Statement
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.