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Measuring Participation in Distance Education Online Discussion Forums Using Social Network Analysis

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dc.contributor.author Cal da Silva, Luiz Fernando
dc.contributor.author Barbosa, Marcelo Werneck
dc.contributor.author Richard Gomes, Rodrigo
dc.date.accessioned 2021-12-07T00:59:21Z
dc.date.available 2021-12-07T00:59:21Z
dc.date.issued 2018-05-27
dc.identifier.citation . DOI: 10.1002/asi.24080 en_US
dc.identifier.uri ${sadil.baseUrl}/handle/123456789/1467
dc.description 12 pages : PDF en_US
dc.description.abstract Distance Education professionals have been constantly coming up with methods and techniques to increase student participation in an environment where learning happens continuously and asynchronously. An online discussion forum (ODF) is one of these mechanisms, but it will only be successful if students are willing to participate. Stimulating students is a challenge many institutions currently face. The objective of this study was to analyze the social interaction among participants in ODFs using Social Network Analysis. Knowing the characteristics of these networks and its participants is important to design actions to improve the use of ODFs. As a case study, data were collected from ODF logs of the majors in Business Administration and Accounting in a Brazilian private university. This study found out that these interaction networks are sparse, which shows that students could be more engaged in interacting and collaborating with others. Students, in general, tend to interact more in the first semester and interaction diminishes as time passes. The number of active ODF participants has been around 45–50%, which shows that students currently do not participate very often in ODFs. Their main incentive seems to exist when they are graded. Popular ODFs were also analyzed. en_US
dc.language.iso en en_US
dc.publisher John Wiley & Sons en_US
dc.subject Education online, Social Network analysis, Participation and Interactions. en_US
dc.title Measuring Participation in Distance Education Online Discussion Forums Using Social Network Analysis en_US
dc.type Article en_US


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