Predictors of Student Academic Success in the Corequisite Model

The purpose of this study was to determine predictors of community college student academic success in corequisite English and mathematics courses. Academic success was defined dichotomously on a pass or fail basis. The population included 1,934 students enrolled in at least one corequisite English...

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Autores principales:	Damon Andrews, Steven Tolman
Formato:	article
Lenguaje:	EN
Publicado:	Georgia Southern University 2021
Materias:	developmental education corequisite model remediation community college students logistic regression mathematics english gateway courses predictors high school gpa Education (General) L7-991
Acceso en línea:	https://doaj.org/article/f030516c6f5b43269ff4e6f8e85a7c5e
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spelling	oai:doaj.org-article:f030516c6f5b43269ff4e6f8e85a7c5e2021-11-10T19:05:42ZPredictors of Student Academic Success in the Corequisite Model2330-726910.20429/gcpa.2021.370203https://doaj.org/article/f030516c6f5b43269ff4e6f8e85a7c5e2021-11-01T00:00:00Zhttps://digitalcommons.georgiasouthern.edu/gcpa/vol37/iss2/3https://doaj.org/toc/2330-7269The purpose of this study was to determine predictors of community college student academic success in corequisite English and mathematics courses. Academic success was defined dichotomously on a pass or fail basis. The population included 1,934 students enrolled in at least one corequisite English and/or mathematics course at a community college between the fall semester of 2015 and summer semester of 2018. Binary logistic regression was used to examine the fol- lowing predictors: a student’s sex, race, age at time of enrollment, Pell Grant recipient status, first-generation college student status, high school grade point average (HSGPA), placement test scores, academic major, time spent receiving academic tutoring; and corequisite course faculty employment status. The two strongest predictors of student academic success in corequisite English courses were: (1) HSGPA and (2) being female. The three strongest predictors of student academic success in corequisite mathematics courses were: (1) HSGPA, (2) corequisite course faculty employment status, and (3) mathematics course based on major. The strongest predictor in both logistic regression analyses was HSGPA. It is recommended that educational leaders use HSGPA as a metric for placing students in the corequisite model. Additionally, it is recommended that institutions continue to invest in faculty professional development opportunities as it relates to teaching students who are non-female, minority, economically-disadvantaged, or first-generation.Damon AndrewsSteven TolmanGeorgia Southern Universityarticledevelopmental educationcorequisite modelremediationcommunity college studentslogistic regressionmathematicsenglishgateway coursespredictorshigh school gpaEducation (General)L7-991ENGeorgia Journal of College Student Affairs, Vol 37, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	developmental education corequisite model remediation community college students logistic regression mathematics english gateway courses predictors high school gpa Education (General) L7-991
spellingShingle	developmental education corequisite model remediation community college students logistic regression mathematics english gateway courses predictors high school gpa Education (General) L7-991 Damon Andrews Steven Tolman Predictors of Student Academic Success in the Corequisite Model
description	The purpose of this study was to determine predictors of community college student academic success in corequisite English and mathematics courses. Academic success was defined dichotomously on a pass or fail basis. The population included 1,934 students enrolled in at least one corequisite English and/or mathematics course at a community college between the fall semester of 2015 and summer semester of 2018. Binary logistic regression was used to examine the fol- lowing predictors: a student’s sex, race, age at time of enrollment, Pell Grant recipient status, first-generation college student status, high school grade point average (HSGPA), placement test scores, academic major, time spent receiving academic tutoring; and corequisite course faculty employment status. The two strongest predictors of student academic success in corequisite English courses were: (1) HSGPA and (2) being female. The three strongest predictors of student academic success in corequisite mathematics courses were: (1) HSGPA, (2) corequisite course faculty employment status, and (3) mathematics course based on major. The strongest predictor in both logistic regression analyses was HSGPA. It is recommended that educational leaders use HSGPA as a metric for placing students in the corequisite model. Additionally, it is recommended that institutions continue to invest in faculty professional development opportunities as it relates to teaching students who are non-female, minority, economically-disadvantaged, or first-generation.
format	article
author	Damon Andrews Steven Tolman
author_facet	Damon Andrews Steven Tolman
author_sort	Damon Andrews
title	Predictors of Student Academic Success in the Corequisite Model
title_short	Predictors of Student Academic Success in the Corequisite Model
title_full	Predictors of Student Academic Success in the Corequisite Model
title_fullStr	Predictors of Student Academic Success in the Corequisite Model
title_full_unstemmed	Predictors of Student Academic Success in the Corequisite Model
title_sort	predictors of student academic success in the corequisite model
publisher	Georgia Southern University
publishDate	2021
url	https://doaj.org/article/f030516c6f5b43269ff4e6f8e85a7c5e
work_keys_str_mv	AT damonandrews predictorsofstudentacademicsuccessinthecorequisitemodel AT steventolman predictorsofstudentacademicsuccessinthecorequisitemodel
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Predictors of Student Academic Success in the Corequisite Model

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