Refining the Process of the Commandant's Education Board

Abstract

As warfare becomes increasingly complex, the Marine Corps needs to better equip its warriors. One of the ways the Marine Corps does this is through graduate education programs (GEP). The Commandants Education Board assesses candidates based on their desire, career timing, and aptitude. Aptitude is broken into competitiveness and competency. Desire is assessed via a survey. Competitiveness is assessed via fitness reports (FITREP). However, the Marine Corps has no effective method of assessing competency in the field for which a Marine is attending a GEP. This thesis used data from the Marine Corps Total Force Data Warehouse, specifically the Marine-1 and Master Brief Sheet data, to find predictors for FITREP performance of Marines filling 88xx billets. These predictors were taken from before the Marine entered into a Marine Corps GEP. This study focused on the following predictors: source of entry, time in service, primary military occupational specialty (PMOS), level of previous education (undergraduate versus graduate), and undergraduate major subject (STEM or liberal arts). This study found that source of entry, PMOS, and STEM had no predictive power in determining a Marines performance. It also found that greater time in service and some graduate education had a slightly positive correlation to performance. This thesis recommends discontinuing vetting Marines for STEM undergraduates and giving preference to Marines with previous graduate education.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2023
Accession Number
AD1213588

Entities

People

  • Brent J. Niewoehner

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • California
  • Classification
  • Data Analysis
  • Data Mining
  • Data Sets
  • Education
  • Engineering
  • Machine Learning
  • Military Education
  • Operations Research
  • Reserve Officer Training Corps
  • Service Academies
  • Students
  • Supervised Machine Learning
  • United States
  • United States Naval Academy
  • Warfare

Readers

  • Naval Personnel Management
  • STEM Education