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2015 » Papers » Volume 1 » HOW DOES TIME SHAPE A VIRTUAL COMMUNITY OF PRACTICE? 1. HOW DOES TIME SHAPE A VIRTUAL COMMUNITY OF PRACTICE? Authors: Stavarache Larise, Trausan-Matu Stefan, Dascalu Mihai, Nistor Nicolae Volume 1 | DOI: 10.12753/2066-026X-15-056 | Pages: 380-386 | Download PDF | Abstract
Informal and self-regulated learning are the processes that trigger learners to pursue their goals and transform their day to day questions and reflections into thoughts oriented towards achieving results. Nowadays, most people take advantage of online resources through opinion sharing or by taking part in building knowledge and expertise while debating different topics of interest. Online knowledge building communities (oKBC) emerge and evolve in a similar manner to face-to-face communities, but their specificity relies on the fact that they are developed on, and maintained using technologies over the web. The concept of oKBC is intertwined with concepts such as shared interests, shared expertise, building social connections and growing the knowledge about the topic, all orbiting around social collaboration. This process of pulling and pushing information drives collaboration through all the stages of personal development, assimilation and evolution. In this paper we aim to observe the development of virtual communities, integrative and non-integrative, following a multitude of dimensions: analysis of members' activity, detection of the degree of involvement within the community, finding key topics that can change the normal routine of the community, and creating an interaction network that connects the members and links the underlying information. Finally, as main scope of this paper we set the target to observe the timeline evolution of these communities by crawling them on a regular basis and by using a predefined crawling schema that is preserved throughout the analysis period. A crawling schema is defined by: preserving the infrastructure parameters of crawling, respecting the time quota for the crawling process and through structure preservation of the information extracted. | Keywords
textual complexity model, MOOC, comparative traits, discourse analysis |
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