Temporal networks in collaborative learning: a case study

Mohammed Saqr, Ward Peeters

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


Social Network Analysis (SNA) has enabled researchers to understand and optimize the key dimensions of collaborative learning. A majority of SNA research has so far used static networks, ie, aggregated networks that compile interactions without considering when certain activities or relationships occurred. Compressing a temporal process by discarding time, however, may result in reductionist oversimplifications. In this study, we demonstrate the potentials of temporal networks in the analysis of online peer collaboration. In particular, we study: (1) social interactions by analysing learners' collaborative behaviour, part of a case study in which they worked on academic writing tasks, and (2) cognitive interactions through the analysis of students' self-regulated learning tactics. The study included 123 students and 2550 interactions. By using temporal networks, we show how to analyse the longitudinal evolution of a collaborative network visually and quantitatively. Correlation coefficients with grades, when calculated with time-respecting temporal measures of centrality, were more correlated with learning outcomes than traditional centrality measures. Using temporal networks to analyse the co-temporal and longitudinal development, reach, and diffusion patterns of students' learning tactics has provided novel insights into the complex dynamics of learning, not commonly offered through static networks.

Original languageEnglish
Pages (from-to)1283-1303
Number of pages21
JournalBritish Journal of Educational Technology
Issue number5
Publication statusPublished - Sept 2022
Externally publishedYes


  • collaborative learning
  • CSCL
  • learning analytics
  • social network analysis
  • temporal networks
  • uptake

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