Doctoral Dissertations

Keywords and Phrases

Degree completion; Machine learning; Prediction model; Student success; Systems architecture

Abstract

“ The goals of higher education have evolved through time based on the impact that technology development and industry have on productivity. Nowadays, jobs demand increased technical skills, and the supply of prepared personnel to assume those jobs is insufficient. The system of higher education needs to evaluate their practices to realize the potential of cultivating an educated and technically skilled workforce. Currently, completion rates at universities are too low to accomplish the aim of closing the workforce gap. Recent reports indicate that 40 percent of freshman at four-year public colleges will not graduate, and rates of completion are even lower for community colleges. Some efforts have been made to adjust admission requirements and develop systems of support for different segments of students; however, completion rates are still considered low. Therefore, new strategies need to consider student success as part of the institutional culture based on the information technology support. Also, it is key that the models that evaluate student success can be scalable to other higher education institutions. In recent years machine learning techniques have proven to be effective for such purpose. Then, the primary objective of this research is to develop an integrated system that allows for the application of machine learning for student success prediction. The proposed system was evaluated to determine the accuracy of student success predictions using several machine learning techniques such as decision trees, neural networks, support vector machines, and random forest. The research outcomes offer an important understanding about how to develop a more efficient and responsive system to support students to complete their educational goals”--Abstract, page iv.

Advisor(s)

Cudney, Elizabeth A.

Committee Member(s)

Dagli, Cihan H., 1949-
Kwasa, Benjamin J.
Murray, Susan L.
Ludlow, Douglas K.

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2020

Journal article titles appearing in thesis/dissertation

  • Data mining and machine learning retention models in higher education, A systematic review
  • Higher education student success: a system to evaluate degree completion
  • Predicting degree completion through data mining
  • Predicting student retention using artificial neural networks
  • Predicting student retention using support vector machines
  • Predicting student degree completion using random forest

Pagination

xv, 136 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2020 Tatiana Alejandra Cardona Sepulveda, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11737

Electronic OCLC #

1198498983

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