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How About Us? Exploring the Relationship Between College Promise Programs and Part-Time Student Success.

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Title: How About Us? Exploring the Relationship Between College Promise Programs and Part-Time Student Success.
Authors: Sublett, Cameron1 (AUTHOR) csublett@utk.edu, Rauner, Mary2 (AUTHOR)
Source: Community College Review. Oct2025, Vol. 53 Issue 4, p411-433. 23p.
Subject Terms: *Part-time students, *Community colleges, *Retention of college students, *Academic achievement, *Associate degree education, *Educational attainment, Data mining
Abstract: Objective: A growing body of research has examined the relationship between participation in college promise programs (CPP) and student outcomes, including college enrollment, persistence, and completion. However, variation in CPP design limits the extent to which study findings are generalizable. Further, most studies analyze the outcomes of student participation in the whole program rather than specific program features. This study explored the relationship between CPP participation and degree completion among part-time students. Method: Analyses relied on administrative data from a single community college located in the western United States. Estimates were derived from a matched sample and a series of linear probability models. Results: Results showed that CPP participants were significantly more likely than the comparison group to enroll full-time for at least one semester after initially enrolling on a part-time basis. Results also indicated that all participants were significantly more likely to earn transfer-oriented associate's degrees and certificates than their peers in the comparison group. Importantly, while full-time attendance was associated with increased probabilities of completion for all program participants, the relationship was moderated by CPP participation. [ABSTRACT FROM AUTHOR]
Copyright of Community College Review is the property of Sage Publications Inc. 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.)
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  Data: How About Us? Exploring the Relationship Between College Promise Programs and Part-Time Student Success.
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  Data: <searchLink fieldCode="AR" term="%22Sublett%2C+Cameron%22">Sublett, Cameron</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> csublett@utk.edu</i><br /><searchLink fieldCode="AR" term="%22Rauner%2C+Mary%22">Rauner, Mary</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Community+College+Review%22">Community College Review</searchLink>. Oct2025, Vol. 53 Issue 4, p411-433. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Part-time+students%22">Part-time students</searchLink><br />*<searchLink fieldCode="DE" term="%22Community+colleges%22">Community colleges</searchLink><br />*<searchLink fieldCode="DE" term="%22Retention+of+college+students%22">Retention of college students</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Associate+degree+education%22">Associate degree education</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+attainment%22">Educational attainment</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink>
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  Data: Objective: A growing body of research has examined the relationship between participation in college promise programs (CPP) and student outcomes, including college enrollment, persistence, and completion. However, variation in CPP design limits the extent to which study findings are generalizable. Further, most studies analyze the outcomes of student participation in the whole program rather than specific program features. This study explored the relationship between CPP participation and degree completion among part-time students. Method: Analyses relied on administrative data from a single community college located in the western United States. Estimates were derived from a matched sample and a series of linear probability models. Results: Results showed that CPP participants were significantly more likely than the comparison group to enroll full-time for at least one semester after initially enrolling on a part-time basis. Results also indicated that all participants were significantly more likely to earn transfer-oriented associate's degrees and certificates than their peers in the comparison group. Importantly, while full-time attendance was associated with increased probabilities of completion for all program participants, the relationship was moderated by CPP participation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Community College Review is the property of Sage Publications Inc. 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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        Value: 10.1177/00915521251335004
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      – Code: eng
        Text: English
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        PageCount: 23
        StartPage: 411
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      – SubjectFull: Part-time students
        Type: general
      – SubjectFull: Community colleges
        Type: general
      – SubjectFull: Retention of college students
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      – SubjectFull: Academic achievement
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      – SubjectFull: Associate degree education
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      – SubjectFull: Educational attainment
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      – SubjectFull: Data mining
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              Text: Oct2025
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