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Identification of At-Risk Students U...

Little, Laura M.

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Identification of At-Risk Students Using Preenrollment Data at a 2-Year Career and Technical College.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identification of At-Risk Students Using Preenrollment Data at a 2-Year Career and Technical College./
作者:	Little, Laura M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	161 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: A.
Contained By:	Dissertations Abstracts International82-06A.
標題:	Educational administration. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28260533
ISBN:	9798698593850

Identification of At-Risk Students Using Preenrollment Data at a 2-Year Career and Technical College.
Little, Laura M.

Identification of At-Risk Students Using Preenrollment Data at a 2-Year Career and Technical College. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 161 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: A.

Thesis (D.B.A.)--Wilmington University (Delaware), 2020.

This item must not be sold to any third party vendors.

Graduation rates at a 2-year, private, nonprofit career and technical college decreased from 73% to 61% between 2016 and 2018. To reverse this trend, a proactive approach to student support is needed to help at-risk students before they sit in their first class. The purpose of this ex post facto data study was to identify risk factors associated with low graduation rates. Student data from the fall of 2014 to the fall of 2018 (n = 1,246) was used to detect significant relationships between 17 independent variables and two dependent variables. Statistical analysis identified the following variables as significantly related to graduation and/or college grade point average (CGPA): Ethnicity, age, socioeconomic status, gender, zip code, transfer status, first-generation status, commute distance, high school, number of high school math classes, highest level of high school math, grades earned in high school math, SAT/ACT participation, GED status, college program, number of days between application and enrollment (AppDays), and high school grade point average (HSGPA). Binomial logistic regression identified six independent variables as being predictive of graduation. Multiple linear regression identified seven independent variables as being predictive of CGPA. Recommendations for the college include developing a survey to identify nontangible data points as well as investigating why differences in socioeconomic status do not affect graduation rates. Future research opportunities include the concepts of AppDays or participating in non-required SAT/ACT exams as indicators of ambition or self-motivation.

ISBN: 9798698593850Subjects--Topical Terms:

2122799
Educational administration.
Subjects--Index Terms:

At-risk students

Identification of At-Risk Students Using Preenrollment Data at a 2-Year Career and Technical College.
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500 $a Source: Dissertations Abstracts International, Volume: 82-06, Section: A.
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520 $a Graduation rates at a 2-year, private, nonprofit career and technical college decreased from 73% to 61% between 2016 and 2018. To reverse this trend, a proactive approach to student support is needed to help at-risk students before they sit in their first class. The purpose of this ex post facto data study was to identify risk factors associated with low graduation rates. Student data from the fall of 2014 to the fall of 2018 (n = 1,246) was used to detect significant relationships between 17 independent variables and two dependent variables. Statistical analysis identified the following variables as significantly related to graduation and/or college grade point average (CGPA): Ethnicity, age, socioeconomic status, gender, zip code, transfer status, first-generation status, commute distance, high school, number of high school math classes, highest level of high school math, grades earned in high school math, SAT/ACT participation, GED status, college program, number of days between application and enrollment (AppDays), and high school grade point average (HSGPA). Binomial logistic regression identified six independent variables as being predictive of graduation. Multiple linear regression identified seven independent variables as being predictive of CGPA. Recommendations for the college include developing a survey to identify nontangible data points as well as investigating why differences in socioeconomic status do not affect graduation rates. Future research opportunities include the concepts of AppDays or participating in non-required SAT/ACT exams as indicators of ambition or self-motivation.
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