In the past few years, we have written up a bulk of work to support and guide teaching of network analysis in the domains related to educational research and modelling of digital networks. Since the nature of publications differs from the intentions of the authors, below, we outline these papers with annotations on their relevance/significance for teaching network analysis of structures constructed from digital traces. We recommend you also look at the empirical studies we have been doing in the area of modelling learner networks from digital trace data.

• Poquet, O., Saqr, M., Chen, B. (2021) Recommendations for Network Research in Learning Analytics: To Open a Conversation. In O. Poquet, B. Chen, T. Hecking, M. Saqr (eds.) CEUR-WS Proceedings of the NetSciLA2021 Workshop "Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda" (NetSciLA2021), Newport Beach, CA, USA (virtual), 34–41. Aachen: CEUR Workshop Proceedings.
• Poquet, O. , Joksimovic. S. (2022). Cacophony of networks in learning analytics. In Eds. C. Lang, G. Siemens, A. Wise, D. Gašević, A. Merceron, Handbook of Learning Analytics, 2nd Edition. Society for Learning Analytics and Research.
• Chen, B., & Poquet, O. (2022). Networks in learning analytics: Where theory, methodology, and practice intersect. Journal of Learning Analytics, 9(1), 1-12.
• Poquet, O., Chen, B. The theories of social networks and learning analytics. 2023. To appear in K. Bartimote, S. Howard, D. Gasevic (Eds). Theory Informing and Arising from Learning Analytics. Springer New York.
• Saqr, M., Poquet, O., & López-Pernas, S. (2022). Networks in education: A travelogue through five decades. IEEE Access, 10, 32361-32380.