Detecting Self-Regulated Learning in Online Communities by Means of Interaction Analysis

Interaction analysis is increasingly used to study learning dynamics within online communities. This paper aims to investigate whether interaction analysis can help understand the practice and development of self-regulated learning (SRL) in virtual learning communities (VLCs). To this end, a set of SRL indicators is proposed to spot clues of self-regulated events within students´ messages. Such clues have been identified and classified according to Zimmerman´s SRL model and some subsequent studies concerning SRL in technology enhanced learning environments (TELEs). They have been tested on the online component of a blended course for trainee teachers, by analyzing the messages exchanged by a group of learners in two modules of the course. The results of this analysis have been compared with those of a previous study carried out, with more traditional methods, on the same course. The similarity of the results obtained by the two approaches suggests that interaction analysis is an effective, though rather labor-intensive, methodology to study SRL in online learning communities.