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Community values: Exploring what undergraduate engineering students value in their community college experience.

  • Published In: New Directions for Community Colleges, 2023, v. 2023, n. 203. P. 155 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Maitra, Debalina; Coley, Brooke; Greene, Clarreese 3 of 3

Abstract

Previous research exploring the lived experiences of marginalized groups of students in STEM at community college settings is limited, despite the fact that community colleges have the potential to create pathways to 4‐year engineering degrees and diversify the STEM workforce. This study explored what underrepresented students in STEM valued most as they navigated community college. We conducted nineteen semi‐structured narrative interviews. Our findings suggested that the students found some of the values implicit compared to other explicit values mentioned by the participants. For example, they valued intimate class size, invested faculty, available tutors, and financial support as explicit values. They appreciated the low‐pressure environment, directional support, networking opportunities, and hands‐on learning as implicit values associated with their college experience. Our research has a direct implication on how such experiences influence students' chosen pathways toward engineering careers, and institutions can learn directly to establish programs with an understanding of what they value. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:New Directions for Community Colleges. 2023/09, Vol. 2023, Issue 203, p155
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2023
  • ISSN:0194-3081
  • DOI:10.1002/cc.20594
  • Accession Number:172913112
  • Copyright Statement:Copyright of New Directions for Community Colleges is the property of Wiley-Blackwell 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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