2020
DOI: 10.1177/0091552120936345
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(Re)Measuring Community College Student Engagement: Testing a Seven-Factor CCSSE Model

Abstract: Objective: Despite the popularity of the Community College Survey of Student Engagement (CCSSE), this instrument has been criticized regarding its development, theoretical basis, validity, and connection to practice. In light of these concerns, this article reframes the survey by employing Chickering and Gamson’s seven principles, one of the survey’s original theoretical frameworks. A robust discussion of the new measure and its implications for research and practice is presented. Method: Chickering a… Show more

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Cited by 8 publications
(3 citation statements)
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“…It encompasses students' engagement in educationally helpful activities and their evaluations of the institutional aspects that promote their learning and progress [58]. McCarrell and Selznick [59] concur with Kuh [58], claiming that student engagement is the result of a dynamic relationship between what students do and what the institution does. It is not something students do or experience, but a reality that is co-created in the classroom by students and faculty members in a setting that is both particular and broad at the same time, rather than a theoretical idea.…”
Section: B Student Engagementmentioning
confidence: 99%
See 1 more Smart Citation
“…It encompasses students' engagement in educationally helpful activities and their evaluations of the institutional aspects that promote their learning and progress [58]. McCarrell and Selznick [59] concur with Kuh [58], claiming that student engagement is the result of a dynamic relationship between what students do and what the institution does. It is not something students do or experience, but a reality that is co-created in the classroom by students and faculty members in a setting that is both particular and broad at the same time, rather than a theoretical idea.…”
Section: B Student Engagementmentioning
confidence: 99%
“…To discover the interesting associative patterns, FP-Growth association rules mining algorithm was implemented using python programming language. In order to transform the dataset into a binomial structure that is compactible for the adopted FP-Growth Association rule mining algorithm each EI score was transformed into one of the following categories: poor (0-29), good (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44) or excellent (45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) depending on the EI score on the rescaled 60-point scale. This representation is consistent with the student academic performance grading system of both universities, with fails (0-49), passes and distinction (75-100).…”
Section: Association Rule Miningmentioning
confidence: 99%
“…A growing body of work about the shortcomings of these models-especially in terms of accommodating the millions of students now attending community colleges, which constitute 39% of the entire undergraduate population in the U.S. today-has made the need for new theoretical models urgent (AACC, 2023;Bailey & Xu, 2012;Bailey et al, 2015;Bean & Metzner, 1985;G. Chen, 2021;Deil-Amen, 2011;Horn et al, 2019;Iloh, 2018;Jabbar et al, 2021;McCarrell & Selznick, 2020;Moore & Shulock, 2009;Sullivan, 2008;Tinto, 1997).…”
Section: Introductionmentioning
confidence: 99%