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Cited by 467

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Online Technologies Self-Efficacy and Self-Regulated Learning as Predictors of Final Grade and Satisfaction in College-Level Online Courses

Puzziferro, Maria·2008·American Journal of Distance Education

Abstract

This study was designed to examine performance as a function of grade and course satisfaction in online undergraduate level courses, specifically students' self-efficacy for online technologies and self-regulated learning strategies. This research included a sample (N = 815) of community college students enrolled in liberal arts online courses during a single semester. The results of this study showed that online technologies self-efficacy scores were not correlated with student performance. Of the Motivated Strategies for Learning Questionnaire subscales, time and study environment and effort regulation were significantly related to performance. Students who scored higher on these subscales received higher final grades. In addition, rehearsal, elaboration, metacognitive self-regulation, and time and study environment were significantly positively correlated with levels of satisfaction.

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Metadata

Authors
Puzziferro, M.
Venue
American Journal of Distance Education
Published
2008-05-14
Volume
22
Issue
2
Pages
72-89
ISSN
  • 0892-3647 (ISSN-L)
  • 1538-9286
OpenAlex
W2004181325
Cited by
467
References
6
FWCI
24.33
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Online Technologies Self-Efficacy and Self-Regulated Learning as Predictors of Final Grade and Satisfaction in College-Level Online Courses. https://app.cochat.ai/papers/10.1080/08923640802039024
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Puzziferro, M. (2008). Online Technologies Self-Efficacy and Self-Regulated Learning as Predictors of Final Grade and Satisfaction in College-Level Online Courses. American Journal of Distance Education. https://doi.org/10.1080/08923640802039024

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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.