An Approach for Assessing Large Online Communities in Informal Learning Environments

Abstract

Online Learning Communities (OLC), supported by social web technologies, have proved to be beneficial for collaborative knowledge building, mainly in informal environments. There is an increasing interest in assessing online Social Learning (SL) in these communities. However, there is no agreement on how their performance can be measured. This paper presents an approach which combines structure and discourse analyses to assess large online communities used in SL. Its objective is to identify conditions and behavioral patterns associated to learning. The results point out a set of quantitative features which shows that participation and ongoing collaboration have a fundamental role for knowledge creation and sharing.

Analyzing learners' behavior and discourse within large online communities: a Social Learning Analytics Dashboard

“…Redditors are also able to assign points to each other responses. These points, named karma, indicate the members' expertise and reflect their popularity [Silva et al 2020]. Discussion score and karma points comprise the Reddit peer assessment data.…”

“…Stage 2 has fitted three models created by Silva et al (2020) that identified the significant structured and discourse measures associated with the best rated discussions. These models are described as follow:…”