The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Online Learning Readiness
Abstract
This paper examines the relationship between student characteristics of online learning readiness and student outcomes in online courses at two higher educational institution. Data were collected from student surveys (student characteristics of readiness and outcomes) and merged with institutional student information systems data (e.g., demographics and course grades). Multiple regression analyses revealed that several student characteristics of online learning readiness significantly influenced student outcomes. MANOVA analyses were conducted to examine between group differences of each student characteristics among underrepresented student groups. Significant findings are reported for minorities and for students with disabilities.
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Metadata
- Authors
- Joosten, T., & Cusatis, R.
- Affiliations
- University of Wisconsin–Milwaukee (US)
- Corresponding author
- Joosten, Tanya
- Venue
- American Journal of Distance Education
- Publisher
- Informa UK Limited
- Published
- 2020-02-21
- Volume
- 34
- Issue
- 3
- Pages
- 180-193
- ISSN
- 0892-3647 (ISSN-L)
- 1538-9286
- OpenAlex
- W3006741760
- Cited by
- 175
- References
- 12
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 27 · 2023: 46 · 2022: 40 · 2021: 43 · 2020: 5
- FWCI
- 50.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Distance Education (publishedVersion)
- Funders
- Office of Postsecondary Education (P116Q140006)
- Topics
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Education › Higher Education Research Studies
- Social Sciences › Education › Education during COVID-19 pandemic
- Keywords
- e-learning readiness, online courses, student outcomes, student characteristics, underrepresented students, minority students, students with disabilities, higher education, course grades
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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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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