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Cited by 32

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

Use of Avatars and a Virtual Community to Increase Cultural Competence

Lee, Eun-Kyoung Othelia·2014·Journal of Technology in Human Services

Abstract

This study compared learning objectives achieved in hybrid/online social work courses through MSW student use of avatars and virtual communities (n = 25) to those achieved in a traditional course (n = 22). Learning outcomes were compared through students' reflections on “cocktail party” exercises occurring either in virtual communities or in class. The potential for and barriers to successful implementation of virtual communities in social work education are discussed. In summary, virtual communities and avatars present a vital opportunity for individuals to experience different perspectives, which, in turn, can nurture culturally competent skill building.

Overview

Integrity signals

Registry

Crossref · 10.1080/15228835.2013.860364

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

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Claims other papers make about this one

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Citations

Cites

Research works printed but not linked: 2

  1. 10.4101/jvwr.v1i1.283 · DOI · Paper page
  2. 10.2307/23043864 · doi:10.2307/23043864

Cited by

  • Mechanizing People and Pedagogy: Establishing Social Presence in the Online Classroom

    Cunningham, J. M. (2015) · Online Learning · doi:10.24059/olj.v19i3.476 · Cited by 30

    “Exploring the effectiveness of using avatars for pedagogical purposes when teaching social work students about cultural competence, Lee (2014) considered using Second Life because it is “the most sophisticated virtual community on the market,” but chose to incorporate Voki, because it required less…”
    Background

See all 32

Metadata

Authors
Lee, E.-K. O.
Affiliations
University of North Carolina Charlotte
Venue
Journal of Technology in Human Services
Published
2014-01
Volume
32
Issue
1-2
Pages
93-107
ISSN
  • 1522-8835
  • 1522-8991
Cited by
32
References
26
Language
en
Funders
This research is funded by Chancellor's Diversity Challenge Fund and the Scholarship for Teaching and Learning at University of North Carolina at Charlotte.

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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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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.