Cited by 135Online and Blended LearningOnline Learning and AnalyticsEvasion and Academic Success Factors

Identifying at‐risk students in videoconferencing and web‐based distance education

Osborn, Viola·2001·American Journal of Distance Education

Abstract

Based on the multivariate framework of student attrition developed by leading researchers in the field of distance education and instructional technology, this study centers on a method of assessing the ability of a student to complete a distance learning course. The focus of the investigation is construction and validation of a brief survey instrument to identify at‐risk students enrolled in Web‐based and videoconferencing courses.

Integrity signals

Registry

Crossref · 10.1080/08923640109527073

Cites

14 of 14 references verified0 citation errors · 0 not checkable

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Authors
Osborn, V.
Affiliations
Baylor University (US)
Corresponding author
Osborn, Viola
Venue
American Journal of Distance Education
Published
2001-01
Volume
15
Issue
1
Pages
41-54
ISSN
  • 0892-3647 (ISSN-L)
  • 1538-9286
OpenAlex
W1966767041
Cited by
135
References
16
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 7 · 2021: 10 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 4 · 2016: 12 · 2015: 7 · 2014: 8 · 2013: 6 · 2012: 7
FWCI
20.97
Type
journal article
Language
en
Open access
closed
Locations
American Journal of Distance Education (publishedVersion)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Demography › Evasion and Academic Success Factors
Keywords
at-risk students, student attrition, distance education, videoconferencing, web-based learning, course completion prediction, questionnaire development, instrument validation

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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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