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Cited by 366

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

PREDICTING LEARNING FROM ASYNCHRONOUS ONLINE DISCUSSIONS

Open access
Wu, Dezhi, Hiltz, Starr Roxanne·2019·Online Learning

Abstract

Online discussions play an important role in student learning. Currently, asynchronous learning courses and some primarily face-to-face courses at New Jersey Institute of Technology require asynchronous online discussions. Does this requirement improve the students’ perceived learning? An exploratory study designed to investigate this issue was conducted in Spring 2002. The questionnaire drew responses to a post-course questionnaire from 116 students in two undergraduate courses and one graduate course. A set of items designed to measure perceived learning from online discussions formed a reliable, unidimensional index for this construct. The results indicate that online discussions do improve students’ perceived learning. Variations among instructors or courses are associated with differences in perceptions of student motivation, enjoyment, and learning from online discussion. Open-ended questions identified some student concerns about online discussions. The concluding section of this paper presents some implications for improving online discussions and for future research plans.

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Crossref · 10.24059/olj.v8i2.1832

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Metadata

Authors
Wu, D., & Hiltz, S. R.
Venue
Online Learning
Published
2019-03-19
Volume
8
Issue
2
ISSN
  • 1092-8235 (ISSN-L)
  • 2472-5730
  • 2472-5749
OpenAlex
W155460434
Cited by
366
References
22
FWCI
84.13
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: PREDICTING LEARNING FROM ASYNCHRONOUS ONLINE DISCUSSIONS. https://app.cochat.ai/papers/10.24059/olj.v8i2.1832
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Wu, D., & Hiltz, S. R. (2019). PREDICTING LEARNING FROM ASYNCHRONOUS ONLINE DISCUSSIONS. Online Learning. https://doi.org/10.24059/olj.v8i2.1832

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.