Using Interaction Analysis to Reveal Self-Regulated Learning in Virtual Communities

Abstract

Aim of this paper is to analyse whether Interaction Analysis can help investigate the practice and development of Self-Regulated Learning (SRL) in Virtual Learning Communities (VLC). Interaction analysis is increasingly used to study learning dynamics within online activities. It proceeds by searching expressions that reveal the aspects under study in the written messages exchanged by the learners. To this end, we devised and classified a number of indicators suggesting the existence of self-regulated events, and tested this approach on the online component of a blended course for trainee teachers. We analysed the messages exchanged by a group of learners in two modules of the course and compared the results with those of a previous study carried out with more traditional methods. The similarity of the results obtained by the two approaches suggests that Interaction Analysis is an effective, though rather labour-intensive, way to study SRL in VLCs.