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Predicting Academic Outcomes of Rein...

Stene Winkler, Erica E.

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Predicting Academic Outcomes of Reinstated Technical College Students following an Academic Suspension.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Academic Outcomes of Reinstated Technical College Students following an Academic Suspension./
Author:	Stene Winkler, Erica E.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	231 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
Subject:	Higher education. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27961831
ISBN:	9781083498410

Predicting Academic Outcomes of Reinstated Technical College Students following an Academic Suspension.
Stene Winkler, Erica E.

Predicting Academic Outcomes of Reinstated Technical College Students following an Academic Suspension. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 231 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ed.D.)--Minnesota State University, Mankato, 2020.

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

This study explored the academic outcomes of two-year college students who were reinstated following an academic suspension at a small, Midwestern technical college. Binary logistic regression was used to identify factors that were predictive of student reinstatement outcomes (reinstatement success or reinstatement nonsuccess). The analysis included independent variables of age, gender, cumulative quality point status, term of dismissal quality point status, and evidence of a mental health concern. As an independent variable, evidence of a mental health concern was dichotomous (yes or no) and a qualitative review of suspension appeal paperwork submitted by students was used to identify any self-reported or documented evidence of a mental health concern. The overall model, which included all independent variables, was found to be statistically significant and correctly predicted 65.7% of all cases. A significant relationship was also identified between student reinstatement outcomes and the independent variables of age and cumulative quality point status. In accordance with the findings of this study, limitations, recommendations for future research, and implications for future practice are discussed.

ISBN: 9781083498410Subjects--Topical Terms:

641065
Higher education.
Subjects--Index Terms:

Academic reinstatement

Predicting Academic Outcomes of Reinstated Technical College Students following an Academic Suspension.
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520 $a This study explored the academic outcomes of two-year college students who were reinstated following an academic suspension at a small, Midwestern technical college. Binary logistic regression was used to identify factors that were predictive of student reinstatement outcomes (reinstatement success or reinstatement nonsuccess). The analysis included independent variables of age, gender, cumulative quality point status, term of dismissal quality point status, and evidence of a mental health concern. As an independent variable, evidence of a mental health concern was dichotomous (yes or no) and a qualitative review of suspension appeal paperwork submitted by students was used to identify any self-reported or documented evidence of a mental health concern. The overall model, which included all independent variables, was found to be statistically significant and correctly predicted 65.7% of all cases. A significant relationship was also identified between student reinstatement outcomes and the independent variables of age and cumulative quality point status. In accordance with the findings of this study, limitations, recommendations for future research, and implications for future practice are discussed.
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