Exploring Online Course Sociograms Using Cohesion Network Analysis

Maria-Dorinela Sirbu, Mihai Dascalu, Scott A. Crossley, Danielle S. McNamara, T. Barnes, C.F. Lynch, Stefan Trausan-Matu

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Abstract

    Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and to
    identify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.
    Original languageEnglish
    Title of host publicationArtificial Intelligence in Education
    Subtitle of host publication19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II
    EditorsC.P. Rosé, R. Martínez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, B. d. Boulay
    Place of PublicationCham
    PublisherSpringer
    Pages337–342
    Number of pages6
    VolumePart II
    ISBN (Electronic)9783319938462
    ISBN (Print)9783319938455
    DOIs
    Publication statusPublished - 2018
    EventInternational Conference, AIED 2018 - London, United Kingdom
    Duration: 27 Jun 201830 Jun 2018
    https://aied2018.utscic.edu.au/

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume10948

    Conference

    ConferenceInternational Conference, AIED 2018
    Abbreviated titleAIED 2018
    CountryUnited Kingdom
    CityLondon
    Period27/06/1830/06/18
    Internet address

    Fingerprint

    sociogram
    network analysis
    visualization
    learning environment
    student
    interaction pattern
    learning success
    ability
    interaction
    drop-out
    discourse
    trend
    Teaching
    learning
    performance

    Keywords

    • Cohesion Network Analysis
    • Online courses
    • Sociograms
    • Participants clustering
    • Interaction patterns

    Cite this

    Sirbu, M-D., Dascalu, M., Crossley, S. A., McNamara, D. S., Barnes, T., Lynch, C. F., & Trausan-Matu, S. (2018). Exploring Online Course Sociograms Using Cohesion Network Analysis. In C. P. Rosé, R. Martínez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, ... B. D. Boulay (Eds.), Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II (Vol. Part II, pp. 337–342). (Lecture Notes in Computer Science; Vol. 10948). Cham: Springer. https://doi.org/10.1007/978-3-319-93846-2_63
    Sirbu, Maria-Dorinela ; Dascalu, Mihai ; Crossley, Scott A. ; McNamara, Danielle S. ; Barnes, T. ; Lynch, C.F. ; Trausan-Matu, Stefan. / Exploring Online Course Sociograms Using Cohesion Network Analysis. Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. editor / C.P. Rosé ; R. Martínez-Maldonado ; U. Hoppe ; R. Luckin ; M. Mavrikis ; K. Porayska-Pomsta ; B. McLaren ; B. d. Boulay. Vol. Part II Cham : Springer, 2018. pp. 337–342 (Lecture Notes in Computer Science).
    @inproceedings{c3ce468de57b432cbcad85fe27236e8a,
    title = "Exploring Online Course Sociograms Using Cohesion Network Analysis",
    abstract = "Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and toidentify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.",
    keywords = "Cohesion Network Analysis, Online courses, Sociograms, Participants clustering, Interaction patterns",
    author = "Maria-Dorinela Sirbu and Mihai Dascalu and Crossley, {Scott A.} and McNamara, {Danielle S.} and T. Barnes and C.F. Lynch and Stefan Trausan-Matu",
    note = "This publication reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains.",
    year = "2018",
    doi = "10.1007/978-3-319-93846-2_63",
    language = "English",
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    series = "Lecture Notes in Computer Science",
    publisher = "Springer",
    pages = "337–342",
    editor = "C.P. Ros{\'e} and R. Mart{\'i}nez-Maldonado and U. Hoppe and R. Luckin and M. Mavrikis and K. Porayska-Pomsta and B. McLaren and Boulay, {B. d. }",
    booktitle = "Artificial Intelligence in Education",

    }

    Sirbu, M-D, Dascalu, M, Crossley, SA, McNamara, DS, Barnes, T, Lynch, CF & Trausan-Matu, S 2018, Exploring Online Course Sociograms Using Cohesion Network Analysis. in CP Rosé, R Martínez-Maldonado, U Hoppe, R Luckin, M Mavrikis, K Porayska-Pomsta, B McLaren & BD Boulay (eds), Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. vol. Part II, Lecture Notes in Computer Science, vol. 10948, Springer, Cham, pp. 337–342, International Conference, AIED 2018, London, United Kingdom, 27/06/18. https://doi.org/10.1007/978-3-319-93846-2_63

    Exploring Online Course Sociograms Using Cohesion Network Analysis. / Sirbu, Maria-Dorinela; Dascalu, Mihai; Crossley, Scott A. ; McNamara, Danielle S.; Barnes, T.; Lynch, C.F.; Trausan-Matu, Stefan.

    Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. ed. / C.P. Rosé; R. Martínez-Maldonado; U. Hoppe; R. Luckin; M. Mavrikis; K. Porayska-Pomsta; B. McLaren; B. d. Boulay. Vol. Part II Cham : Springer, 2018. p. 337–342 (Lecture Notes in Computer Science; Vol. 10948).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    TY - GEN

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    AU - Dascalu, Mihai

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    AU - McNamara, Danielle S.

    AU - Barnes, T.

    AU - Lynch, C.F.

    AU - Trausan-Matu, Stefan

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    N2 - Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and toidentify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.

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    Sirbu M-D, Dascalu M, Crossley SA, McNamara DS, Barnes T, Lynch CF et al. Exploring Online Course Sociograms Using Cohesion Network Analysis. In Rosé CP, Martínez-Maldonado R, Hoppe U, Luckin R, Mavrikis M, Porayska-Pomsta K, McLaren B, Boulay BD, editors, Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. Vol. Part II. Cham: Springer. 2018. p. 337–342. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-93846-2_63