What Predicts the Momentum of Information and Communications Technologies Students in Community College?

Jill Denner (), Susan Potter, Pamela Anderson and David Torres
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Jill Denner: Education, Training, Research
Susan Potter: Education, Training, Research
Pamela Anderson: Education, Training, Research
David Torres: Education, Training, Research

Research in Higher Education, 2023, vol. 64, issue 5, No 1, 623-653

Abstract: Abstract There is persistent underrepresentation of female and ethnic and racial minority students in computing. While community colleges provide a unique opportunity to increase diversity in computing fields, many students do not persist. This study aims to understand the factors that predict students’ momentum—completion of a certificate, degree, or transfer in an information and communications technology (ICT) major—in order to generate information that can be used to tailor interventions. Participants were enrolled in ICT classes at 17 community colleges. Surveys were collected from 474 students at three time points over two years. Multilevel logistic regression was used to identify the factors that predict momentum approximately one year after the class ended. The results expand on Wang’s theoretical model of student momentum. Men were more likely than women to have achieved an academic milestone, which was partially a result of taking more prior ICT classes, having more positive interactions with faculty and a more positive perception of the classroom climate, as well as greater motivation and fewer childcare responsibilities. Among students from groups that are underrepresented in computing, momentum was positively associated with taking prior ICT classes, motivation during the class, and romantic relationships; it was negatively associated with educational barriers. Being enrolled less than full-time at baseline or having financial challenges did not undermine momentum. Implications for practice, as well as study limitations are discussed.

Keywords: Community college; Momentum; Information technology; Longitudinal (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11162-022-09721-8

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