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Cited by 278

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Computer self‐efficacy, academic self‐concept, and other predictors of satisfaction and future participation of adult distance learners

Lim, Christina Kyounghee·2001·American Journal of Distance Education

Abstract

The purpose of this study was to develop a predictive model of satisfaction of adult learners in a Web‐based distance education course and their intent to participate in future Web‐based distance education courses. The factors examined were computer self‐efficacy, academic self‐concept, age, gender, academic status, years of computer use, frequency of computer use, computer training, Internet experience in a class, and participation in a workshop for a Web‐based course. Computer self‐efficacy was the only predictor variable that was statistically significant. There was a positive relationship between learners' satisfaction with their Web‐based distance education courses and their intent to participate in future Web‐based courses.

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Metadata

Authors
Lim, C. K.
Venue
American Journal of Distance Education
Published
2001-01
Volume
15
Issue
2
Pages
41-51
ISSN
  • 0892-3647 (ISSN-L)
  • 1538-9286
OpenAlex
W2087274771
Cited by
278
References
21
FWCI
31.14
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Computer self‐efficacy, academic self‐concept, and other predictors of satisfaction and future participation of adult distance learners. https://app.cochat.ai/papers/10.1080/08923640109527083
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Lim, C. K. (2001). Computer self‐efficacy, academic self‐concept, and other predictors of satisfaction and future participation of adult distance learners. American Journal of Distance Education. https://doi.org/10.1080/08923640109527083

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.