Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis

Nicolae Nistor, Mihai Dascălu, Lucia Larise Stavarache, Christian Tarnai, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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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 peripherality. 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.
Original languageEnglish
Title of host publicationState-of-the-Art and Future Directions of Smart Learning
EditorsYanyan Li, Maiga Chang, Milos Kravcik, Elvira Popescu, Ronghuai Huang, Kinshuk, Nian-Shing Chen
PublisherSpringer Singapore
ISBN (Electronic)978-981-287-868-7
ISBN (Print)978-981-287-866-3, 978-981-10-1313-3
Publication statusPublished - 27 Oct 2015
Externally publishedYes

Publication series

SeriesLecture Notes in Educational Technology


  • knowledge communities
  • newcomer integration
  • dialog analysis
  • social learning analytics


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