COMPARISON OF ARTIFICIAL INTELLIGENCE METHODS TO ENHANCE AN AUTOMATED PEER-EVALUATION SUITE

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dc.contributor.advisorRowe, Neil C.
dc.contributor.advisorDas, Arijit
dc.contributor.authorNelson, Andrew E.
dc.contributor.departmentComputer Science (CS)
dc.dateSep-20
dc.date.accessioned2020-11-18T00:23:12Z
dc.date.available2020-11-18T00:23:12Z
dc.date.issued2020-09
dc.description.abstractA 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.en_US
dc.description.distributionstatementApproved for public release. distribution is unlimiteden_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceMajor, United States Marine Corpsen_US
dc.identifier.curriculumcode368, Computer Science
dc.identifier.thesisid34358
dc.identifier.urihttps://hdl.handle.net/10945/66116
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis 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 Statesen_US
dc.subject.authorpeer evaluationen_US
dc.subject.authorperformance feedbacken_US
dc.subject.authorUnited States Marine Corpsen_US
dc.subject.authorUSMCen_US
dc.subject.authorOfficer Candidates Schoolen_US
dc.subject.authorOCSen_US
dc.subject.authorcounselingen_US
dc.subject.authorartificial intelligenceen_US
dc.subject.authordatabaseen_US
dc.subject.authordata synthesisen_US
dc.subject.authorentry level training.en_US
dc.titleCOMPARISON OF ARTIFICIAL INTELLIGENCE METHODS TO ENHANCE AN AUTOMATED PEER-EVALUATION SUITEen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.nameMaster of Science in Computer Scienceen_US
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