COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS TO ENHANCE AN AUTOMATED PEER-EVALUATION SUITE
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Authors
Nelson, Andrew E.
Subjects
peer evaluation
performance feedback
United States Marine Corps
USMC
Officer Candidates School
OCS
counseling
artificial intelligence
database
data synthesis
entry level training.
performance feedback
United States Marine Corps
USMC
Officer Candidates School
OCS
counseling
artificial intelligence
database
data synthesis
entry level training.
Advisors
Rowe, Neil C.
Das, Arijit
Date of Issue
2020-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
A Department of Defense strategic focus area for artificial intelligence is the better allocation of personnel resources. The current peer-evaluation system at the Marine Officer Candidates School could benefit from artificial intelligence methods to partially automate the process. The school identifies performance trends by summarizing peer inputs and providing useful feedback to candidates to improve performance. This thesis used data from a recent training company and applied natural-language processing to preprocess peer inputs, identified phrases most helpful in predicting overall performance, extracted the best sentences for characterizing a candidate, and assembled draft counseling documents that required minimal revision by staff. Experiments with a prototype of our methods on a sample of real peer evaluations and summary counseling documents showed good though not perfect performance.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
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. Copyright protection is not available for this work in the United States