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Using agglomerative hierarchical clustering to model learner participation profiles in online discussion forums

Published: 29 April 2012 Publication History

Abstract

Online discussion forums are a key element in virtual learning environments. The way learners participate in discussion boards can be a very useful source of indicators for teachers to facilitate their tasks. The use of a two-stage analysis strategy based on an agglomerative hierarchical clustering algorithm is proposed in this paper to identify different participation profiles adopted by learners in online discussion forums. Different parameters are used to characterize learners' activity (amount of posts, rhythm, depth of threads, crossed replies, etc). Participation profiles are identified and analyzed in terms of behavior and performance.

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  • (2022)Academic Performance Evaluation Using Data Mining in Times of PandemicTECHNO REVIEW. International Technology, Science and Society Review /Revista Internacional de Tecnología, Ciencia y Sociedad10.37467/gkarevtechno.v11.332411:1(89-106)Online publication date: 3-Aug-2022
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        cover image ACM Conferences
        LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
        April 2012
        282 pages
        ISBN:9781450311113
        DOI:10.1145/2330601
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        New York, NY, United States

        Publication History

        Published: 29 April 2012

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        Author Tags

        1. educational data mining
        2. hierarchical clustering
        3. learner behavior modeling
        4. learning analytics
        5. online discussion forums

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        LAK 2012
        Sponsor:
        • SIGWEB
        • TEKRI
        • Desire2Learn
        • EDUCAUSE
        • University of British Columbia
        LAK 2012: Second International Conference on Learning Analytics and Knowledge
        April 29 - May 2, 2012
        British Columbia, Vancouver, Canada

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        Overall Acceptance Rate 236 of 782 submissions, 30%

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        View all
        • (2024)Partition-Based Clustering Algorithms Applied to Mixed Data for Educational Data Mining: A Survey From 1971 to 2024IEEE Access10.1109/ACCESS.2024.349692912(172923-172942)Online publication date: 2024
        • (2023)Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive AnalysisIEEE Access10.1109/ACCESS.2023.332762411(120192-120208)Online publication date: 2023
        • (2022)Academic Performance Evaluation Using Data Mining in Times of PandemicTECHNO REVIEW. International Technology, Science and Society Review /Revista Internacional de Tecnología, Ciencia y Sociedad10.37467/gkarevtechno.v11.332411:1(89-106)Online publication date: 3-Aug-2022
        • (2021)Teachers’ Ontological Perspectives of Computational Thinking and Assessment: A Text Mining ApproachJournal of Educational Computing Research10.1177/0735633121104354760:3(661-695)Online publication date: 3-Sep-2021
        • (2018)Performance Prediction in Online Discussion Forum: state-of-the-art and comparative analysisProcedia Computer Science10.1016/j.procs.2018.08.178135(302-314)Online publication date: 2018
        • (2018)Application of learning analytics using clustering data Mining for Students' disposition analysisEducation and Information Technologies10.1007/s10639-017-9645-723:2(957-984)Online publication date: 1-Mar-2018
        • (2018)Semantically Meaningful Cohorts Enable Specialized Knowledge Sharing in a Collaborative MOOCLifelong Technology-Enhanced Learning10.1007/978-3-319-98572-5_28(370-384)Online publication date: 14-Aug-2018
        • (2017)A Systematic Review on Educational Data MiningIEEE Access10.1109/ACCESS.2017.26542475(15991-16005)Online publication date: 2017
        • (2016)Implementing cluster analysis tool for the identification of students typologies2016 4th IEEE International Colloquium on Information Science and Technology (CiSt)10.1109/CIST.2016.7804852(575-580)Online publication date: Oct-2016
        • (2015)Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical ClusteringPLOS ONE10.1371/journal.pone.014175610:10(e0141756)Online publication date: 30-Oct-2015

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