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Evaluating Student Participation in Undergraduate Information Technology Programs in the U.S.

Published:07 October 2020Publication History

ABSTRACT

Enrollment, retention, and graduation rates of undergraduate students in Information Technology (IT) programs are useful measures of institutional performance. Disaggregated by demographics characteristics, such as gender, race, and ethnicity, analysis of student data across IT programs in the U.S. supports the exploration of the breadth and diversity of student participation in IT. Evaluating undergraduate IT programs is particularly challenging for multiple factors, including: IT programs are not always titled "Information Technology"; IT programs are not always ABET-accredited; and IT programs may be housed in various academic units, such as business, computing, engineering, technology, or information sciences.

This paper builds on prior work used to identify IT programs in the U.S., including the National Center for Education Statistics' Classification of Instructional Programs (CIP) codes, specifically CIP code 11 that designates IT and other computing programs. It also refines CIP code-based program identification and then analyzes 2017-2018 student data from the National Student Clearinghouse Research Center to evaluate IT programs through a student participation lens. The in-depth analysis of student enrollment, retention, and graduation is intended to support IT programs with designing more inclusive learning environments that increase participation of all students, in particular women and racial and ethnic minority students. This paper finally signals the importance of CIP codes that designate IT programs and focuses attention to the role that faculty, IT programs, and SIGITE community at large need to have in CIP code selection to further advance research in IT education.

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      cover image ACM Conferences
      SIGITE '20: Proceedings of the 21st Annual Conference on Information Technology Education
      October 2020
      446 pages
      ISBN:9781450370455
      DOI:10.1145/3368308

      Copyright © 2020 ACM

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      Publication History

      • Published: 7 October 2020

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