Incentivizing Full-Time Enrollment at Community Colleges: What Influences Students’ Decision to Take More Courses?

Abstract

Objective: Students’ enrollment intensity during their first academic semester and year are highly influential in their success and decisions to persist in community college. Limited research, however, has focused on understanding how students navigate the decision-making process of determining their credit load or how institutional support services, such as academic advising, influence these decisions. Our objective was to advance knowledge on this topic by examining the implementation of a 15 to Finish campaign to promote full-time enrollment at a large, racially/ethnically diverse community college system in Texas. Methods: We interviewed 24 students and eight academic advisors to identify key factors that influence recommendations for, and selection of, course load. The collection and analysis of the data employed the critical qualitative inquiry method, and data were analyzed through low- and high-level coding processes using reconstructive analysis. Results: The factors influencing student decision-making fall into three major areas: (1) academic performance, (2) financial considerations and other costs, and (3) advising experiences, with academic performance framing students’ discussion. Academic advisors agreed with the importance of full-time enrollment for students’ success and recognized the importance of reducing time-to-degree, but believed many students were not academically prepared or had too many commitments to enroll in 15 credits each semester. Conclusion: Consequently, a “one-more-course” approach may be more attractive for both students and advisors. We outline recommendations that better enable community college students to make optimal credit load decisions, which in turn can increase their academic momentum and chances for degree completion.

Governing towards democracy: Challenges and opportunities of trustees at urban community colleges

“…Whether in spite of or because of the broad swath of students, programs, contexts, and roles they serve, the influence and potential of community colleges is clear; they educate nearly 40% of all US undergraduates (American Association of Community Colleges [AACC], 2022) and are home to the majority of marginalized students (e.g., students of color) who are enrolled in public postsecondary education (D'Amico et al., 2022; Wood & Newman, 2017). While community colleges are often populated with the largest and most vulnerable populations, the institutions are often plagued with issues of transfer barriers to success (Glynn, 2019; Goldrick‐Rab, 2010), inconsistent enrollment (McKinney et al., 2022), disproportionate placement of marginalized groups in below‐basic courses (Chen, 2016) and a host of other issues.…”

“…It would be prudent to assess how governing boards influence policy and whether changes in governance may advance the attainment agenda (White, 2019). At present, our most marginalized students disproportionately enroll in community colleges that often struggle to provide the guidance and structure so sorely needed by these groups (McKinney et al., 2022). Though well‐known for their commitment to open access (Hagedorn et al., 2008), community colleges often struggle with graduation outcomes (Reyes et al., 2019) and in the urban sector specifically, inequity exists in myriad forms (Bing et al, 2020).…”

Sentiment analysis and opinion mining on educational data: A survey

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ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

“…can be exploited to understand their opinion [14]. It can help educational institutions make optimal and timely measures to improve learning, engagement and deliveries [15]. Educational institutions can exploit student's feedback after every semester to understand their opinion for the courses enrolled and the deliveries made [15].…”

“…It can help educational institutions make optimal and timely measures to improve learning, engagement and deliveries [15]. Educational institutions can exploit student's feedback after every semester to understand their opinion for the courses enrolled and the deliveries made [15]. Specifically, the use of statistical data as quantitative tool can be applied for student's feedback assessment, while qualitative feedback can be exploited by NLP and AI to understand their opinion.…”