Transfer Student Receptivity in Patriarchal STEM Contexts: Evidence of Gendered Transfer Student Stigma in Computer Science From a Mixed Methods Study.

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Title: Transfer Student Receptivity in Patriarchal STEM Contexts: Evidence of Gendered Transfer Student Stigma in Computer Science From a Mixed Methods Study.
Authors: Blaney, Jennifer M.1 (AUTHOR) jennifer.blaney@nau.edu, Hernandez, Theresa E.1 (AUTHOR), Feldon, David F.2 (AUTHOR), Wofford, Annie M.3 (AUTHOR)
Source: Community College Review. Jan2025, Vol. 53 Issue 1, p28-60. 33p.
Subject Terms: *Transfer of students, *Computer science students, *Transfer students, *Computer science, Science journalism, Sex discrimination
Abstract: Research Questions: While community college transfer (i.e., upward transfer) represents an important mechanism for advancing equity across STEM fields, existing studies of gender and women's participation within computer science have largely excluded the perspectives of upward transfer students. We address this gap in the literature by exploring transfer receptivity and gender discrimination within computer science, guided by the following questions: (1) How do upward transfer computer science students report their receptivity experiences, and how might this differ by gender? (2) How do upward transfer computer science students make meaning of receptivity experiences, and how might that meaning making be shaped by gender? Methods: We use a sequential mixed methods design, relying on longitudinal survey and interview data from upward transfer computer science majors, collected throughout students' first year at the receiving university. Results: Findings reveal that, relative to men, upward transfer women report greater experiences of transfer stigma and challenges accessing resources at the receiving university. Qualitative findings document additional nuances in how upward transfer students—especially women—describe resilience as they navigate the university campus, encounter navigation challenges at the university, and make meaning of various manifestations of transfer stigma on campus. Contributions: In addition to implications for research and theory, we discuss what universities can do to foster a more receptive environment for upward transfer women. Specific recommendations focus on ensuring that spaces for women in computing are inclusive of transfer students and, likewise, creating supportive transfer cohort communities that are inclusive of women. [ABSTRACT FROM AUTHOR]
Copyright of Community College Review is the property of Sage Publications Inc. 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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  Data: Transfer Student Receptivity in Patriarchal STEM Contexts: Evidence of Gendered Transfer Student Stigma in Computer Science From a Mixed Methods Study.
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  Data: <searchLink fieldCode="JN" term="%22Community+College+Review%22">Community College Review</searchLink>. Jan2025, Vol. 53 Issue 1, p28-60. 33p.
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  Data: *<searchLink fieldCode="DE" term="%22Transfer+of+students%22">Transfer of students</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+science+students%22">Computer science students</searchLink><br />*<searchLink fieldCode="DE" term="%22Transfer+students%22">Transfer students</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Science+journalism%22">Science journalism</searchLink><br /><searchLink fieldCode="DE" term="%22Sex+discrimination%22">Sex discrimination</searchLink>
– Name: Abstract
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  Data: Research Questions: While community college transfer (i.e., upward transfer) represents an important mechanism for advancing equity across STEM fields, existing studies of gender and women's participation within computer science have largely excluded the perspectives of upward transfer students. We address this gap in the literature by exploring transfer receptivity and gender discrimination within computer science, guided by the following questions: (1) How do upward transfer computer science students report their receptivity experiences, and how might this differ by gender? (2) How do upward transfer computer science students make meaning of receptivity experiences, and how might that meaning making be shaped by gender? Methods: We use a sequential mixed methods design, relying on longitudinal survey and interview data from upward transfer computer science majors, collected throughout students' first year at the receiving university. Results: Findings reveal that, relative to men, upward transfer women report greater experiences of transfer stigma and challenges accessing resources at the receiving university. Qualitative findings document additional nuances in how upward transfer students—especially women—describe resilience as they navigate the university campus, encounter navigation challenges at the university, and make meaning of various manifestations of transfer stigma on campus. Contributions: In addition to implications for research and theory, we discuss what universities can do to foster a more receptive environment for upward transfer women. Specific recommendations focus on ensuring that spaces for women in computing are inclusive of transfer students and, likewise, creating supportive transfer cohort communities that are inclusive of women. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Community College Review is the property of Sage Publications Inc. 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.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/00915521231218233
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      – SubjectFull: Computer science students
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      – SubjectFull: Transfer students
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      – SubjectFull: Sex discrimination
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              Text: Jan2025
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