Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 377

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

NETWORK ANALYSIS OF KNOWLEDGE CONSTRUCTION IN ASYNCHRONOUS LEARNING NETWORKS

Open access
Aviv, Reuven, Erlich, Zippy, Ravid, Gilad, Geva, Aviva·2019·Online Learning

Abstract

Asynchronous Learning Networks (ALNs) make the process of collaboration more transparent, because a transcript of conference messages can be used to assess individual roles and contributions and the collaborative process itself. This study considers three aspects of ALNs: the design; the quality of the resulting knowledge construction process; and cohesion, role and power network structures. The design is evaluated according to the Social Interdependence Theory of CooperativeLearning. The quality of the knowledge construction process is evaluated through Content Analysis; and the network structures are analyzed using Social Network Analysis of the response relations among participants during online discussions. In this research we analyze data from two three-monthlong ALN academic university courses: a formal, structured, closed forum and an informal, nonstructured, open forum. We found that in the structured ALN, the knowledge construction processreached a very high phase of critical thinking and developed cohesive cliques. The students took on bridging and triggering roles, while the tutor had relatively little power. In the non-structured ALN, the knowledge construction process reached a low phase of cognitive activity; few cliques were constructed; most of the students took on the passive role of teacher-followers; and the tutor was at the center of activity. These differences are statistically significant. We conclude that a well-designed ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.24059/olj.v7i3.1842

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 26 references

Cited by

See all 377

Metadata

Authors
Aviv, R., Erlich, Z., Ravid, G., & Geva, A.
Venue
Online Learning
Published
2019-03-19
Volume
7
Issue
3
ISSN
  • 1092-8235 (ISSN-L)
  • 2472-5730
  • 2472-5749
OpenAlex
W2136345557
Cited by
377
References
26
FWCI
94.74
Type
journal article
Language
en
Open access
diamond

About these checks

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: NETWORK ANALYSIS OF KNOWLEDGE CONSTRUCTION IN ASYNCHRONOUS LEARNING NETWORKS. https://app.cochat.ai/papers/10.24059/olj.v7i3.1842
The paper
Aviv, R., Erlich, Z., Ravid, G., & Geva, A. (2019). NETWORK ANALYSIS OF KNOWLEDGE CONSTRUCTION IN ASYNCHRONOUS LEARNING NETWORKS. Online Learning. https://doi.org/10.24059/olj.v7i3.1842

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.