A learning analytics methodology for understanding social interactions in MOOCs

Abstract

One of the characteristics of MOOCs (Massive Open Online Courses) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer learners behaviour and outcomes. It is not feasible for teachers to process all forum messages and automated tools and analysis are required. Although there are some tools for analysing learners interactions, there is a need for methodologies and integrated tools that help to interpret the learning process based on social interactions in the forum. This work presents the 3S (Social, Sentiments, Skills) learning analytics methodology for analysing forum interactions in MOOCs. This methodology considers a temporal analysis combining the social, sentiments and skill dimensions that can be extracted from forum data. We also introduce LAT'S, a Learning Analytics tool for edX / Open edX related to the three dimensions (3S), which includes visualisations to guide the proposed methodology. We apply the 3S methodology and the tool to a MOOC on Java programming. Results showed, among others, the action-reaction effect produced when learners increase their participation after instructor's events. Moreover, a decrease of positive sentiments over time and before deadlines of open-ended assignments was also observed and that there were certain skills which caused more troubles (e.g., arrays and loops). These results acknowledge the importance of using a learning analytics methodology to detect problems in MOOCs.