Abstract
Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA-related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique. Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviors. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA.
Highlights
Following the extensive use of digital technology in education, a growing field of learning analytics (LA) has emerged since 2011
We reviewed the proceedings of the In ternational Learning Analytics and Knowledge (LAK) Conference to identify relevant studies, as this is a key venue for LA research (Adeniji, 2019)
We provide a summary of the current state of the inherent Social learning analytics (SLA) studies
Summary
Following the extensive use of digital technology in education, a growing field of learning analytics (LA) has emerged since 2011. LA studies have increasingly made use of methodologies that go beyond educational data mining and auto mated discovery, introducing approaches such as social network anal ysis (SNA), discourse analysis, natural language processing, and multimodal LA [32]. In this regard, as a broad interdisciplinary com munity, LA research is focused on a range of epistemologies, ontological approaches, and methods (Author B, 2020). Based on combining principles of networked learning approaches and computer-supported collaborative learning (CSCL), methods of learning analytics can be employed to provide information about group in teractions in social settings at multiple levels of abstraction and how these could be used to support teaching and learning processes
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