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Abstract Informal learning in online knowledge building communities (OKBCs) often starts with online academic help seeking, and with visitor inquiries on specific topics. In such a context, learning presupposes adequate OKBC response. Employing a social learning analytics approach based on natural language processing and Bakhtin's theory of dialogism, this study aims to predict blogger OKBC response. Manipulating the blog topic (well-defined vs. ill-defined) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25,303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of the experimental groups, however, this remained true only for the well-defined topic, whereas for the ill-defined topic the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support newcomer integration in OKBCs and, from a larger perspective, the use of OKBCs as components of formal learning environments. Highlights • We examined collaborative dialog in online knowledge building communities. • Community response is an essential prerequisite of informal learning. • Automated dialog analysis based on Bakhtin's dialogism was employed. • For all and well-defined topics, only inquiry format influenced community response. • For ill-defined topics only dialog quality influenced community response. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]