Date of Award

December 2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Educational Foundations & Research

First Advisor

Robert Stupnisky

Abstract

With the recent development of curricular analytics tools designed to quantify the structure of a curriculum (Heileman, Abdallah, et al., 2018), some institutions of higher education are examining their curricula to find ways that these pathways to graduation can be improved (McMurtrie, 2021). The tools used to analyze curricula were created to address an issue of importance in the field of undergraduate engineering, that issue being the decreasing enrollment trend over the past two decades. The decrease in undergraduate engineering graduates in programs across the United States led to conversations in both academia and industry regarding potential solutions for this issue (Heileman et al., 2017).The purpose of this research study was to explore relationships among variables as they relate to overall curricular complexity. This study sought to explore how curricular complexity relates to data available from the Integrated Postsecondary Education Data System (IPEDS) specific to institutional-level and student-level characteristics. Prior research has not specifically examined a number of potential variables in IPEDS to determine how they relate to curricular complexity. This exploratory study used a sample consisting of 102 undergraduate mechanical engineering programs accredited by the Accreditation Board for Engineering and Technology (ABET). The variables in this study explored both institutional-level and student-level data, including outcome, enrollment, completion, and financial variables at the student-level. A variety of statistical tests were conducted, including descriptive statistics, Pearson correlations, bivariate regression, and multiple regression. A software package created for the Julia language was used to calculate curricular complexity for this study (Heileman, Free, et al., 2018). Results were mixed, but this study found that some variables from IPEDS significantly predicted curricular complexity, while other variables, like those calculated as part of the curricular analytics metrics, such as the longest path length, were stronger predictors of curricular complexity. Results were presented for each research question, which concluded with the creation of a multiple regression model. The final multiple regression model was created to determine if any of the variables selected from IPEDS significantly predicted curricular complexity. The final model with the greatest amount of parsimony was established with five predictor variables.

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