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Analytics and patterns of knowledge creation: Experts at work in an online engineering community
ARTICLE

, New York City College of Technology, United States ; , George Mason University, United States ; , Virginia Tech, United States

Computers & Education Volume 112, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Online learning communities have gained popularity amongst engineering learners who seek to build knowledge and share their expertise with others; yet to date, limited research has been devoted to the development of analytics for engineering communities. This is addressed through our study of an online engineering community that serves 31,219 engineering learners who contributed 503,908 messages in 65,209 topics. The guiding theoretical framework is the knowledge creation metaphor, which conceptualizes learning as a collaborative process of developing shared knowledge artifacts for the collective benefit of a community of learners. The aims of this study are twofold: (1) to analyze the state of knowledge creation in the community; and (2) to evaluate the strength of association between proposed analytics and variables indicative of knowledge creation in online environments. Findings suggest that the community is vibrant as a whole but also reveal disparity in participation at the individual level. At the topic-level, knowledge creation activities are strongly associated with Topic Length and moderately associated with Topic Duration. At the individual-level, participation in knowledge creation activities is strongly associated with Individual Total Interactions and weakly associated with Individual Total Membership Period. The implications of the findings are discussed and may provide guidance for educators seeking to adopt learning analytics in online communities.

Citation

Teo, H.J., Johri, A. & Lohani, V. (2017). Analytics and patterns of knowledge creation: Experts at work in an online engineering community. Computers & Education, 112(1), 18-36. Elsevier Ltd. Retrieved August 20, 2019 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2017.04.011

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