The purpose of this quantitative study was to examine what type of predictive power exists between an instructor’s employment classification, student gender, student race, and first-generation status on a student’s academic success in developmental mathematics, as measured by final semester grades at a regionally comprehensive state university in Texas between fall 2013 and spring 2017. Data were collected from the institution under study and the sample population included 1932 unique student observations. The data collected in this study were analyzed through a binary logistic regression model to determine whether an instructor’s employment classification, student gender, student race, and first-generation status could predict academic success in developmental math. The results of this study showed that a correlation does exist between an instructor’s employment classification, specifically as related to Graduate Teaching Assistants and Adjunct Instructors in being statistically significant to a student’s success in developmental mathematics. Additionally, student race, student gender, and first-generation status showed that a correlation does exist in predicting a student’s success in developmental mathematics, all of which were found to be statistically significant. The findings and conclusions of this study have implications for post-secondary math educators and higher education administrators.
Keywords: higher education, developmental math, student success, first-generation students, instructor employment classification, binary logistic regression
","abstract_html":"<p>The purpose of this quantitative study was to examine what type of predictive power exists between an instructor’s employment classification, student gender, student race, and first-generation status on a student’s academic success in developmental mathematics, as measured by final semester grades at a regionally comprehensive state university in Texas between fall 2013 and spring 2017. Data were collected from the institution under study and the sample population included 1932 unique student observations. The data collected in this study were analyzed through a binary logistic regression model to determine whether an instructor’s employment classification, student gender, student race, and first-generation status could predict academic success in developmental math. The results of this study showed that a correlation does exist between an instructor’s employment classification, specifically as related to Graduate Teaching Assistants and Adjunct Instructors in being statistically significant to a student’s success in developmental mathematics. Additionally, student race, student gender, and first-generation status showed that a correlation does exist in predicting a student’s success in developmental mathematics, all of which were found to be statistically significant. The findings and conclusions of this study have implications for post-secondary math educators and higher education administrators.</p> <p><em>Keywords: higher education, developmental math, student success, first-generation students, instructor employment classification, binary logistic regression</em></p>","abstract_has_math":false,"creators":["Fish, Brittany A."],"institution":null,"degree_name":"Doctor of Education","degree_level":"Dissertation","degree_discipline":"Secondary Education and Educational Leadership","degree_department":null,"school":null,"contributors":["Patrick M. Jenlink, Ed.D.","Scott Bailey, Ed.D.","Keith Hubbard, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-12T07:00:00Z","date_published":"2018-05-12T07:00:00Z","updated_at":"2026-07-24T04:30:15Z","subjects":["higher education","developmental math","student success","first-generation students","instructor employment classification","binary logistic regression","Higher Education Administration","Higher Education and Teaching","Other Mathematics","Science and Mathematics Education"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/163","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Patrick M. 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Data were collected from the institution under study and the sample population included 1932 unique student observations. The data collected in this study were analyzed through a binary logistic regression model to determine whether an instructor’s employment classification, student gender, student race, and first-generation status could predict academic success in developmental math. The results of this study showed that a correlation does exist between an instructor’s employment classification, specifically as related to Graduate Teaching Assistants and Adjunct Instructors in being statistically significant to a student’s success in developmental mathematics. Additionally, student race, student gender, and first-generation status showed that a correlation does exist in predicting a student’s success in developmental mathematics, all of which were found to be statistically significant. The findings and conclusions of this study have implications for post-secondary math educators and higher education administrators.
Keywords: higher education, developmental math, student success, first-generation students, instructor employment classification, binary logistic regression
"]},{"key":"dc:title","label":"Title","values":["Developmental Mathematics: A Quantitative Investigation of Instructor Classification as Related to Student Success"]}]}],"canonical_facts":{"dc:contributor":["Patrick M. Jenlink, Ed.D.","Scott Bailey, Ed.D.","Keith Hubbard, Ph.D."],"dc:creator":["Fish, Brittany A."],"dc:date.available":["2018-05-11T07:00:00Z"],"dc:description.abstract":["The purpose of this quantitative study was to examine what type of predictive power exists between an instructor’s employment classification, student gender, student race, and first-generation status on a student’s academic success in developmental mathematics, as measured by final semester grades at a regionally comprehensive state university in Texas between fall 2013 and spring 2017. Data were collected from the institution under study and the sample population included 1932 unique student observations. The data collected in this study were analyzed through a binary logistic regression model to determine whether an instructor’s employment classification, student gender, student race, and first-generation status could predict academic success in developmental math. The results of this study showed that a correlation does exist between an instructor’s employment classification, specifically as related to Graduate Teaching Assistants and Adjunct Instructors in being statistically significant to a student’s success in developmental mathematics. Additionally, student race, student gender, and first-generation status showed that a correlation does exist in predicting a student’s success in developmental mathematics, all of which were found to be statistically significant. The findings and conclusions of this study have implications for post-secondary math educators and higher education administrators.
Keywords: higher education, developmental math, student success, first-generation students, instructor employment classification, binary logistic regression
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/163"],"dc:subject":["higher education","developmental math","student success","first-generation students","instructor employment classification","binary logistic regression","Higher Education Administration","Higher Education and Teaching","Other Mathematics","Science and Mathematics Education"],"dc:title":["Developmental Mathematics: A Quantitative Investigation of Instructor Classification as Related to Student Success"],"thesis:degree_discipline":["Secondary Education and Educational Leadership"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Education"]},"updated_at":"2026-07-24T04:30:15Z"}