Predicting newcomer integration in online learning communities: Automated dialog assessment in blogger communities. (April 2020) - Nistor, Nicolae; Dascalu, Mihai; Tarnai, Christian; Trausan-Matu, Stefan
Abstract: Using online learning communities (OLCs) from the Internet as informal learning environments raises the question how likely these communities will integrate learners as new members, i.e., how integrative these OLCs will be. Such prediction is the purpose of the current study. To achieve this, an identification method of the central, intermediate, and peripheral OLC layers is proposed. Based on the CSCL approaches of voices interanimation and polyphony, an advanced natural language processing framework was employed for dialog analysis in N = 20 integrative vs. non-integrative blog-based OLCs involving 2342 users over one year. Hierarchical clusters built upon communicative centrality reflect socio-cognitive structures including central, intermediate, and peripheral OLC members. The resulting clusters were assigned to the central, intermediate and peripheral community layers with 55–100% consistency, whereas most consistent identification criterion of the socio-cognitive OLC structures was the outdegree centrality, followed by the blog owner inclusion, the numbers of participants, eccentricity and indegree centrality. Subsequently, OLC integrativity was predicted with up to 90% accuracy based on topic complexity, socio-cognitive structure, and automatically assessed dialog characteristics. Consequences for further research and educational practice are discussed. Highlights: Using online learning communities for learning is a matter of integrativity. We predicted integrativity, i.e., how likely online communites integrate newcomers. The prediction was based on automated dialog analysis in blogger communities. We analyzed N = 20 blogger communities involving 2342 users. We reached 90% prediction accuracy based on text complexity and other indicators.