Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses

Iulia Pasov, Mihai Dascalu, Nicolae Nistor, Stefan Trausan-Matu

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

    Abstract

    The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students’ participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students’ pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions.
    Original languageEnglish
    Title of host publicationThe Interplay of Data, Technology, Place and People for Smart Learning
    Subtitle of host publicationProceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development
    EditorsHendrik Knoche, Elvira Popescu, Antonio Cartelli
    PublisherSpringer
    Pages177-185
    Number of pages9
    Publication statusPublished - 1 Jun 2018
    Event3rd Int. Conf. on Smart Learning Ecosystems and Regional Development (SLERD 2018): The Interplay of Data, Technology, Place and People for Smart Learning - Aalborg, Denmark
    Duration: 23 May 201825 May 2018

    Publication series

    NameSmart Innovation, Systems and Technologies (SIST)
    PublisherSpringer
    Volume95

    Conference

    Conference3rd Int. Conf. on Smart Learning Ecosystems and Regional Development (SLERD 2018)
    CountryDenmark
    CityAalborg
    Period23/05/1825/05/18

    Fingerprint

    time series
    dialogue
    participation
    student
    chat
    expert
    regression
    classroom

    Keywords

    • CSCL
    • Time series analysis
    • Automated evaluation of participation
    • RAGE

    Cite this

    Pasov, I., Dascalu, M., Nistor, N., & Trausan-Matu, S. (2018). Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses. In H. Knoche, E. Popescu, & A. Cartelli (Eds.), The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development (pp. 177-185). (Smart Innovation, Systems and Technologies (SIST); Vol. 95). Springer.
    Pasov, Iulia ; Dascalu, Mihai ; Nistor, Nicolae ; Trausan-Matu, Stefan. / Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses. The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. editor / Hendrik Knoche ; Elvira Popescu ; Antonio Cartelli. Springer, 2018. pp. 177-185 (Smart Innovation, Systems and Technologies (SIST)).
    @inproceedings{66647daf9bf747be8294961e8704a83b,
    title = "Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses",
    abstract = "The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students’ participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students’ pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions.",
    keywords = "CSCL, Time series analysis, Automated evaluation of participation, RAGE",
    author = "Iulia Pasov and Mihai Dascalu and Nicolae Nistor and Stefan Trausan-Matu",
    year = "2018",
    month = "6",
    day = "1",
    language = "English",
    series = "Smart Innovation, Systems and Technologies (SIST)",
    publisher = "Springer",
    pages = "177--185",
    editor = "Knoche, {Hendrik } and Popescu, {Elvira } and Cartelli, {Antonio }",
    booktitle = "The Interplay of Data, Technology, Place and People for Smart Learning",

    }

    Pasov, I, Dascalu, M, Nistor, N & Trausan-Matu, S 2018, Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses. in H Knoche, E Popescu & A Cartelli (eds), The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. Smart Innovation, Systems and Technologies (SIST), vol. 95, Springer, pp. 177-185, 3rd Int. Conf. on Smart Learning Ecosystems and Regional Development (SLERD 2018), Aalborg, Denmark, 23/05/18.

    Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses. / Pasov, Iulia; Dascalu, Mihai; Nistor, Nicolae; Trausan-Matu, Stefan.

    The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. ed. / Hendrik Knoche; Elvira Popescu; Antonio Cartelli. Springer, 2018. p. 177-185 (Smart Innovation, Systems and Technologies (SIST); Vol. 95).

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

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    T1 - Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses

    AU - Pasov, Iulia

    AU - Dascalu, Mihai

    AU - Nistor, Nicolae

    AU - Trausan-Matu, Stefan

    PY - 2018/6/1

    Y1 - 2018/6/1

    N2 - The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students’ participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students’ pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions.

    AB - The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students’ participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students’ pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions.

    KW - CSCL

    KW - Time series analysis

    KW - Automated evaluation of participation

    KW - RAGE

    M3 - Conference article in proceeding

    T3 - Smart Innovation, Systems and Technologies (SIST)

    SP - 177

    EP - 185

    BT - The Interplay of Data, Technology, Place and People for Smart Learning

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    Pasov I, Dascalu M, Nistor N, Trausan-Matu S. Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses. In Knoche H, Popescu E, Cartelli A, editors, The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. Springer. 2018. p. 177-185. (Smart Innovation, Systems and Technologies (SIST)).