SLIM: a process for analyzing learners' behavior and discourse within large online communities
DOI:
https://doi.org/10.5753/rbie.2022.2614Keywords:
Social learning, Informal learning environments, Online communities, Assessment, ProcessAbstract
Educational researchers have an increasing interest in systematically assessing social learning that takes place in large online communities, nowadays one of the most important producers of Big Data in education. However, there is no agreement on how to measure the performance of such communities in informal learning settings. Assessing online Social Learning (SL) is a complex process that calls for an analytical approach in order to understand the various dimensions of learner discourse and the structure of the social interactions. This paper presents SLIM (Process for assessing online Social Learning within online communities in Informal environments): a process that combines structure and discourse analyses to assess SL indicators within large Online Learning Communities (OLC). Initially, we have used data provided by informal environments to perform Social Network Analysis (SNA) in order to identify conditions and behavioral patterns associated to learning. Next, we have incorporated these data into an unsupervised machine learning method to identify a discourse style related to learning. SLIM has been initially applied to two large online communities from the news sharing site Reddit. We are interested in characterizing and assessing the massively distributed learning, and just-in-time learning associated with the development of sustained online communities in informal environments. The results point out a set of quantitative measures and machine learning models that can be used to outline the evolution of SL indicators over time. They suggest that participation, ongoing collaboration and positive emotion have a fundamental role for knowledge creation and sharing. These findings can be used to take actions in order to regulate social interaction within large OLC.
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