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High resolution temporal network analysis to understand and improve collaborative learning

Published: 23 March 2020 Publication History

Abstract

There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.

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  • (2024)Investigating cognitive engagement patterns in online collaborative learning: a temporal learning analytic studyInteractive Learning Environments10.1080/10494820.2023.229997632:10(6997-7013)Online publication date: 2-Jan-2024
  • (2024)More Data is not Always Better Data: An Exploratory Learning Analytics Study in Early PredictionProceedings of TEEM 202310.1007/978-981-97-1814-6_81(830-838)Online publication date: 5-Aug-2024
  • (2024)Predictive Modelling in Learning Analytics: A Machine Learning Approach in RLearning Analytics Methods and Tutorials10.1007/978-3-031-54464-4_7(197-229)Online publication date: 19-Feb-2024
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    LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
    March 2020
    679 pages
    ISBN:9781450377126
    DOI:10.1145/3375462
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 March 2020

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    Author Tags

    1. collaborative learning
    2. learning analytics
    3. medical education
    4. problem-based learning
    5. social network analysis
    6. temporal networks
    7. temporarily

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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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    • (2024)Investigating cognitive engagement patterns in online collaborative learning: a temporal learning analytic studyInteractive Learning Environments10.1080/10494820.2023.229997632:10(6997-7013)Online publication date: 2-Jan-2024
    • (2024)More Data is not Always Better Data: An Exploratory Learning Analytics Study in Early PredictionProceedings of TEEM 202310.1007/978-981-97-1814-6_81(830-838)Online publication date: 5-Aug-2024
    • (2024)Predictive Modelling in Learning Analytics: A Machine Learning Approach in RLearning Analytics Methods and Tutorials10.1007/978-3-031-54464-4_7(197-229)Online publication date: 19-Feb-2024
    • (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
    • (2024)Social Network Analysis: A Primer, a Guide and a Tutorial in RLearning Analytics Methods and Tutorials10.1007/978-3-031-54464-4_15(491-518)Online publication date: 19-Feb-2024
    • (2023)Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic ModelingJournal of Medical Internet Research10.2196/4564525(e45645)Online publication date: 17-May-2023
    • (2023)The temporal dynamics of online problem-based learning: Why and when sequence mattersInternational Journal of Computer-Supported Collaborative Learning10.1007/s11412-023-09385-118:1(11-37)Online publication date: 10-Mar-2023
    • (2023)Examining the effects of different forms of teacher feedback intervention for learners' cognitive and emotional interaction in online collaborative discussion: A visualization method for process mining based on text automatic analysisEducation and Information Technologies10.1007/s10639-023-12097-629:6(6525-6551)Online publication date: 5-Aug-2023
    • (2023)Exploring the interaction of cognition and emotion in small group collaborative discourse by Heuristic Mining Algorithm (HMA) and Inductive Miner Algorithm (IMA)Education and Information Technologies10.1007/s10639-023-11722-828:10(13153-13178)Online publication date: 24-Mar-2023
    • (2023)Capturing the Sequential Pattern of Students’ Interactions in Computer-Supported Collaborative LearningProceedings of TEEM 202310.1007/978-981-97-1814-6_78(800-809)Online publication date: 25-Oct-2023
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