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PRE-ENTRY ATTRIBUTES AND FRESHMAN SATISFACTION, GRADES, AND ENGAGEMENT AS PREDICTORS OF SIX-YEAR COLLEGE GRADUATION.

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Title: PRE-ENTRY ATTRIBUTES AND FRESHMAN SATISFACTION, GRADES, AND ENGAGEMENT AS PREDICTORS OF SIX-YEAR COLLEGE GRADUATION.
Authors: SHOULDERS, CATHERINE, SIMMONS, LUCAS M., JOHNSON, DONALD M.
Source: College Student Journal. Sep2020, Vol. 54 Issue 3, p327-338. 12p.
Subjects: First year experience programs, College student development programs, College student orientation, Affective education, Self-perception
Abstract: This study investigated a sample of 979 full-time, first semester freshmen (FTFSF) university students using data from an individual institution's National Survey of Student Engagement (NSSE) to determine if university pre-entry admissions data, satisfaction, self-reported grades, or student engagement could be used to predict six-year degree completion. Results indicated that self-reported freshman grades, high school grade-point average (HSGPA), being from a metro area, and satisfaction with college experiences were positive indicators of increased likelihood of graduating within six years. Satisfaction with academic advising and Pell grant eligibility were found to be negative predictors of six-year degree completion. These findings can be used to focus recruiting efforts, as well as identify students who may be at risk of attrition. [ABSTRACT FROM AUTHOR]
Copyright of College Student Journal is the property of Project Innovation Austin LLC and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: PRE-ENTRY ATTRIBUTES AND FRESHMAN SATISFACTION, GRADES, AND ENGAGEMENT AS PREDICTORS OF SIX-YEAR COLLEGE GRADUATION.
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  Data: <searchLink fieldCode="AR" term="%22SHOULDERS%2C+CATHERINE%22">SHOULDERS, CATHERINE</searchLink><br /><searchLink fieldCode="AR" term="%22SIMMONS%2C+LUCAS+M%2E%22">SIMMONS, LUCAS M.</searchLink><br /><searchLink fieldCode="AR" term="%22JOHNSON%2C+DONALD+M%2E%22">JOHNSON, DONALD M.</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22First+year+experience+programs%22">First year experience programs</searchLink><br /><searchLink fieldCode="DE" term="%22College+student+development+programs%22">College student development programs</searchLink><br /><searchLink fieldCode="DE" term="%22College+student+orientation%22">College student orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+education%22">Affective education</searchLink><br /><searchLink fieldCode="DE" term="%22Self-perception%22">Self-perception</searchLink>
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  Label: Abstract
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  Data: This study investigated a sample of 979 full-time, first semester freshmen (FTFSF) university students using data from an individual institution's National Survey of Student Engagement (NSSE) to determine if university pre-entry admissions data, satisfaction, self-reported grades, or student engagement could be used to predict six-year degree completion. Results indicated that self-reported freshman grades, high school grade-point average (HSGPA), being from a metro area, and satisfaction with college experiences were positive indicators of increased likelihood of graduating within six years. Satisfaction with academic advising and Pell grant eligibility were found to be negative predictors of six-year degree completion. These findings can be used to focus recruiting efforts, as well as identify students who may be at risk of attrition. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of College Student Journal is the property of Project Innovation Austin LLC and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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