Social learning analytics

TLDR

SLA examines how learners collaboratively construct knowledge within cultural and social contexts, encompassing both formal and informal online learning environments and their networks. The paper argues that SLA is a distinct subset of learning analytics and reviews key drivers that underscore the importance of social learning. The authors identify five SLA types, describe early implementation on the SocialLearn platform at the UK Open University, and adopt an iterative approach that engages learners in shaping analytics and recommendations.

Abstract

This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.