Using network science in learning analytics: Building bridges towards a common agenda

Abstract

Interest in using networks in the analysis of digital data has long existed in learning analytics (LA). Applications of network science in our field are diverse. Some researchers analyze social settings in online discussions, knowledge building software, and group formation tools. Others use networked techniques to capture epistemic and cognitive processes. Networked approaches have been pioneered for psychometrics, for the analysis of time-series data, and for various types of clustering of relational observations. Finally, modelling of variables where networks are used as representations of causal relations is also gaining traction. Given the diversity of the thematic foci that researchers engage in when applying network science to learning analytics, this workshop aims to identify common challenges experienced through the use of network science methodologies. The workshop will invite researchers working in the area to share their work and reflect on common challenges. We envision themes of causality, linkage between micro- and macro-processes, use of time and space, elements of generalizability and validity to surface in the group discussions. The workshop aims to gather LA scholars to collectively build a solid foundation of advanced network modeling of learning data and shape strategies of future work in this important sub-field of LA. © 2021 Copyright for this paper by its authors.