Date of Award

2022

Publisher

North Dakota State University

Document Type

Dissertation

Degree Awarded

Doctor of Philosophy (PhD)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Nygard, Kendall

Description

Despite the growing global demand for Computer Science (CS) professionals, their high earning potential, and diversified career paths (U.S. BLS 2021, UNESCO 2017), a critical gap exists between enrollment and graduation rates among female students in computing fields across the world (Raigoza 2017, Hailu 2018, UNESCO 2017, Bennedsen and Caspersen 2007). The largest dropout point occurs during the first two years of their CS studies (Giannakos, et al., 2017). The purpose of this parallelly convergent mixed-methods research was to comparatively investigate, describe and analyze factors correlated to the experiences and perceptions of female undergraduates as it relates to their persistence in CS/Software Engineering (SE) degrees, conducted in two public universities in the U.S. & Ethiopia. Anchored in Tinto’s theory of retention, the quantitative part of the study examined three possible predictive factors of success for students who were enrolled in the first two CS/SE courses and evaluated differences between genders and institutions on those factors. Pearson’s correlation coefficient tests were applied to test the hypothesis that the perceptions of Degree’s Usefulness (DU), Previously Acquired Knowledge (PAK) and Cognitive Attitude (CA) correlate to the decision to persist for the research participants. The results showed a statistically significant positive correlation between perceptions of DU, the influence of PAK, and the decision to persist. Two sample t-tests revealed gender and institutional differences exhibited in the influence of PAK and CA. The qualitative part of the study reported 12 contributing factors of success for graduating class of females in CS/SE using a unique approach of sentiment analysis and topic modeling from the domain of Natural Language Processing (NLP) through the interpretation of auto transcribed interview responses.

Rights

NDSU policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/33425

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