Socio-temporal dynamics in peer interaction events

Bodong Chen, Oleksandra Poquet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turnby-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.

Original languageEnglish (US)
Title of host publicationLAK 2020 Conference Proceedings - Celebrating 10 years of LAK
Subtitle of host publicationShaping the Future of the Field - 10th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450377126
StatePublished - Mar 23 2020
Event10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020 - Frankfurt, Germany
Duration: Mar 23 2020Mar 27 2020

Publication series

NameACM International Conference Proceeding Series


Conference10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020

Bibliographical note

Publisher Copyright:
© 2020 Copyright held by the owner/author(s).


  • Digital peer networks
  • Relational event modelling
  • Temporality


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