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Nistor, Nicolae ORCID logoORCID: https://orcid.org/0000-0002-9956-1670; Panaite, Marilena; Dascălu, Mihai and Trăușan-Matu, Ștefan ORCID logoORCID: https://orcid.org/0000-0001-8082-8497 (12. November 2018): Identifying Socio-Cognitive Structures in Online Knowledge Building Communities Using Cohesion Network Analysis. 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timișoara, România, 21-24 September 2017. IEEE Xplore. pp. 271-274

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Online knowledge building communities (OKBCs) prove beneficial in informal learning settings. Extending their use to the instructional design of formal learning environments requires identification methods of the central, intermediate and peripheral community layers. This study proposes such a social learning analytics method, i.e., an automated discourse analysis based on Natural Language Processing. The method was applied to the dialog produced by N = 1,990 participants in 20 blogger communities. Centrality criteria based on Cohesion Network Analysis were highly consistent and could successfully identify the socio-cognitive layers of the analyzed OKBCs. Ongoing research proposes instructional design based on formal learners' interactions with OKBCs.

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