Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies

• Published in: Computers and education open 2022-12, Vol.3, p.100073, Article 100073
• Main Authors: Kaliisa, Rogers, Rienties, Bart, Mørch, Anders I., Kluge, Anders
• Format: Article
• Language: English
• Subjects: Computer-supported collaborative learning; Social learning analytics; Systematic review
• ISSN: 2666-5573; 2666-5573
• DOI: 10.1016/j.caeo.2022.100073

•This is a systematic review for social learning analytics studies.•36 journal articles published between 2011 and 2020 were coded and analyzed.•The application of social learning analytics is mainly in formal and fully online settings and few studies share social learning analytics insights with teachers.•We present methodological, theoretical and practical propositions to advance research, creation of tools and the practice of social learning analytics. 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.