Monitoring collaborative learning activities: exploring the differential value of collaborative flow patterns for learning analytics

Abstract

Collaborative learning flow patterns (CLFPs) en code solutions to recurrent pedagogical problems, which have been successfully applied to the design of learning experiences. However, the pedagogical knowledge encoded in these patterns has seldom been exploited in learning analytics (LA). This paper analyzes four of the most common CLFPs to extract the intrinsic constraints that lead to a successful collaborative learning activity, and use them to enhance existing LA solutions. To understand the added value of applying such codified knowledge in LA, we present evidence from five authentic case studies in which such constraints aided university teachers in monitoring complex collaborative scripts. The results not only illustrate quantitatively such added value but also unearth qualitative benefits, such as raising practitioners awareness about how the current state of activities may affect future phases of the script.