Integrating Theories of Learning and Social Networks in Learning Analytics

Interpersonal, relational, and social factors are fundamental to how people learn. In digital learning, these aspects manifest within dyads, groups, communities, and networks, and are therefore included in theoretical accounts of learning. Independently, social network research that focuses on understanding social groups and networks also offers elaborate theoretical views on social relations. The analysis of digital learning requires the integration of views from both domains. This chapter focuses on how to connect social network analysis with digital learning theories. We argue that inferring social networks from digital trace data requires careful conceptualization of social networks within digital learning theories. We review three approaches to learning, namely, knowledge building, networked learning, and connectivism, and highlight how these lenses implicate different views of social processes and therefore distinct ways social networks can be constructed from learning data. In doing so, we illuminate conceptual distinctions undergirding seemingly identical data sources and illustrate how they can be used to model digital learning processes under close guidance of digital learning theories.