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Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis

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Using online knowledge communities (OKCs) from the Internet as informal learning environments poses the question how likely these communities will be to integrate learners as new members. Such prediction is the purpose of the current study. Based on the approaches of voices interanimation and polyphony, a natural language processing tool was employed for dialog analysis in integrative versus non-integrative blog-based OKCs. Four dialog dimensions were identified: participants’ long-term persistence in the discourse, the community response to their participation, their communicative centrality, and their communicative pe- ripherality. Hierarchical clusters built upon these dimensions reflect socio-cognitive structures including central, regular, and peripheral OKC members. While the socio-cognitive structures did not make a significant difference, integrative OKCs display significantly stronger peripherality, community response, and centrality as compared to non-integrative OKCs.

knowledge communities

newcomer integration

dialog analysis

social learning analytics

Published in:
State-of-the-Art and Future Directions of Smart Learning
Page(s):
13-17

Date of Publication:
30 November -0001

Year of Publication:
2015

{
  • author = {Nicolae Nistor and Mihai Dascalu and Lucia Larise Stavarache and Christian Tarnai and Stefan Trausan-Matu},
  • title = {Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis},
  • journal = {State-of-the-Art and Future Directions of Smart Learning},
  • year = {2015},
  • date = {November 30, -0001},
}
Nicolae Nistor and Mihai Dascalu and Lucia Larise Stavarache and Christian Tarnai and Stefan Trausan-Matu 2015 Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis State-of-the-Art and Future Directions of Smart Learning November 30, -0001

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