Cited by 50Online and Blended LearningHigher Education Research StudiesOnline Learning and Analytics

Which STEM majors enroll in online courses, and why should we care? The impact of ethnicity, gender, and non-traditional student characteristics

Open access
Wladis, Claire, Hachey, Alyse C., Conway, Katherine·2015·Computers & Education

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2015.06.010

Cites

102 of 102 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Latinx and Black STEM majors were significantly less likely to take online courses than their White student peers.

    “Specific to STEM online courses, Wladis et al. (2015b) found that Latinx and Black STEM majors were significantly less likely to take online courses than their White student peers.”
  • Women STEM majors are significantly more likely than men STEM majors to take online courses, even when academic preparation, socioeconomic status, citizenship and English-as-second-language status are controlled.

    “Also using national data, Wladis et al. (2015b*, 2015c*) found that women STEM majors are significantly more likely than men STEM majors to take online courses, even when academic preparation, socioeconomic status (SES), citizenship and English-as-second-language status was controlled.”

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Authors
Wladis, C., Hachey, A. C., & Conway, K.
Affiliations
  • The Graduate Center, CUNY (US)
  • Borough of Manhattan Community College (US)
Corresponding author
Wladis, Claire
Venue
Computers & Education
Publisher
Elsevier BV
Published
2015-09
Volume
87
Pages
285-308
ISSN
0360-1315 (ISSN-L)
OpenAlex
W814924284
Cited by
49
References
103
Citations by year
2026: 2 · 2025: 4 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 1
FWCI
10.00
Type
journal article
Language
en
Open access
Locations
Computers & Education (acceptedVersion)
Funders
  • American Educational Research Association
  • National Science Foundation (DRL-0941014)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Social Sciences › Education › Higher Education Research Studies
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
online course registration, STEM majors, race and ethnicity, gender differences, non-traditional students, Hispanic students, Black students, female students, socioeconomic status, English as a second language, citizenship, undergraduate students

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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