Electrical and Computer Engineering ETDs

Publication Date

Fall 12-17-2016

Abstract

The dissertation addresses different aspects of student success in higher education. Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. However, even the best efforts to improve in these areas may fail if other institutional factors overwhelm their ability to facilitate student progress. This dissertation addresses this issue from the perspective of curriculum structure. The structural properties of individual curricula are studied, and the extent to which this structure impacts student progress is explored. The structure of curricula are studied using actual university data and analyzed by applying different data mining techniques, machine learning methods and graph theory. These techniques and methods provide a mathematical tool to quantify the complexity of a curriculum structure. The results presented in this work show that there is an inverse correlation between the complexity of a curriculum and the graduation rate of students attempting that curriculum. To make it more practical, this study was extended further to implement a number of predictive models that give colleges and universities the ability to track the progress of their students in order to improve retention and graduation rates. These models accurately predict the performance of students in subsequent terms and accordingly could be used to provide early intervention alerts. The dissertation addresses another important aspect related to curricula. Specifically, how course enrollment sequences in a curriculum impact student progress. Thus, graduation rates could be improved by directing students to follow better course sequences. The novelty of the models presented in this dissertation is characterized in introducing graduation rate, for the first time in literature, from the perspective of curricular complexity. This provides the faculty and staff the ability to better advise students earlier in their academic careers.

Document Type

Dissertation

Language

English

Degree Name

Computer Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Gregory Heileman

Second Committee Member

Chaouki T. Abdallah

Third Committee Member

Terry Babbitt

Fourth Committee Member

Christos Christodoulou

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