short-paper

Socio-temporal dynamics in peer interaction events

Authors:

Oleksandra PoquetAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 203 - 208

https://doi.org/10.1145/3375462.3375535

Published: 23 March 2020 Publication History

Abstract

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) turn-by-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.

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Cited By

Saqr MLópez-Pernas STörmänen TKaliisa RMisiejuk KTikka S(2025)Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning ProcessesProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706513(351-361)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706513
Sidorov SEmelianov TMironov SSidorova EKostyukhin YVolkov AOstrovskaya APolezharova L(2024)Network Evolution Model with Preferential Attachment at Triadic Formation StepMathematics10.3390/math1205064312:5(643)Online publication date: 22-Feb-2024
https://doi.org/10.3390/math12050643
Saqr M(2024)Temporal Network Analysis: Introduction, Methods and Analysis with RLearning Analytics Methods and Tutorials10.1007/978-3-031-54464-4_17(541-567)Online publication date: 19-Feb-2024
https://doi.org/10.1007/978-3-031-54464-4_17
Show More Cited By

Index Terms

Socio-temporal dynamics in peer interaction events
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
  2. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites
    2. Data mining
      1. Clustering

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cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

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LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Saqr MLópez-Pernas STörmänen TKaliisa RMisiejuk KTikka S(2025)Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning ProcessesProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706513(351-361)Online publication date: 3-Mar-2025
https://dl.acm.org/doi/10.1145/3706468.3706513
Sidorov SEmelianov TMironov SSidorova EKostyukhin YVolkov AOstrovskaya APolezharova L(2024)Network Evolution Model with Preferential Attachment at Triadic Formation StepMathematics10.3390/math1205064312:5(643)Online publication date: 22-Feb-2024
https://doi.org/10.3390/math12050643
Saqr M(2024)Temporal Network Analysis: Introduction, Methods and Analysis with RLearning Analytics Methods and Tutorials10.1007/978-3-031-54464-4_17(541-567)Online publication date: 19-Feb-2024
https://doi.org/10.1007/978-3-031-54464-4_17
Poquet OTrenholm SSantolini M(2023)Forum posts, communication patterns, and relational structures: A multi-level view of discussions in online coursesEducational technology research and development10.1007/s11423-023-10262-972:5(2655-2678)Online publication date: 28-Jun-2023
https://doi.org/10.1007/s11423-023-10262-9
Chen BChen Z(2023)Why do students reply? Uncovering the socio-semantic entanglement in web annotation activitiesEducation and Information Technologies10.1007/s10639-023-12187-529:8(9747-9771)Online publication date: 19-Sep-2023
https://doi.org/10.1007/s10639-023-12187-5
Saqr MPeeters W(2022)Temporal networks in collaborative learning: A case studyBritish Journal of Educational Technology10.1111/bjet.1318753:5(1283-1303)Online publication date: 10-Feb-2022
https://doi.org/10.1111/bjet.13187
Joksimović SDawson SBarthakur APoquet OWang YMarmolejo-Ramos FSiemens G(2022)Mapping the Landscape of Social and Emotional Learning AnalyticsSocial and Emotional Learning and Complex Skills Assessment10.1007/978-3-031-06333-6_3(27-47)Online publication date: 25-Aug-2022
https://doi.org/10.1007/978-3-031-06333-6_3
Dowell NPoquet O(2021)SCIPComputers in Human Behavior10.1016/j.chb.2021.106709119:COnline publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1016/j.chb.2021.106709
Sidorov SMironov SGrigoriev A(2021)Friendship paradox in growth networks: analytical and empirical analysisApplied Network Science10.1007/s41109-021-00391-66:1Online publication date: 7-Jul-2021
https://doi.org/10.1007/s41109-021-00391-6
Sidorov SMironov SFaizliev AGrigoriev A(2021)Node Degree Dynamics in Complex Networks Generated in Accordance with a Modification of the Triadic Closure ModelMathematical Modeling and Supercomputer Technologies10.1007/978-3-030-78759-2_12(146-153)Online publication date: 24-Jun-2021
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