Doctoral Dissertations

Keywords and Phrases

Academic Analytics; Community College; Mahalanobis Taguchi System; Predictive Analytics; STEM Student Retention

Abstract

"Numerous government reports point to the multifaceted issues facing the country's capacity to increase the number of STEM majors, while also diversifying the workforce. Community colleges are uniquely positioned as integral partners in the higher education ecosystem. These institutions serve as an access point to opportunity for many students, especially underrepresented minorities and women. Community colleges should serve as a major pathway to students pursuing STEM degrees; however student retention and completion rates are dismally low. Therefore, there is a need to predict STEM student success and provide interventions when factors indicate potential failure. This enables educational institutions to better advise and support students in a more intentional and efficient manner. The objective of this research was to develop a model for predicting success. The methodology uses the Mahalanobis Taguchi System as a novel approach to pattern recognition and gives insight into the ability of MTS to predict outcomes based on student demographic data and academic performance. The method accurately predicts institution-specific risk factors that can be used to better retain STEM students. The research indicates the importance of using community college student data to target this distinctive student population that has demonstrated risk factors outside of the previously reported factors in prior research. This methodology shows promise as a mechanism to close the achievement gap and maximize the power of open-access community college pathways for STEM majors"--Abstract, page iv.

Advisor(s)

Cudney, Elizabeth A.

Committee Member(s)

Murray, Susan L.
Konur, Dincer
Ludlow, Douglas K.
Qin, Ruwen

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2018

Journal article titles appearing in thesis/dissertation

  • Retention models for STEM majors and alignment to community colleges: a review of the literature
  • A retention model for community college STEM students
  • A methodology for predicting STEM retention in community colleges

Pagination

x, 87 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Jennifer Lynn Snyder, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11398

Electronic OCLC #

1051222867

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