Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors [discussant]

Ryan S.J.d. Baker, Janice D. Gobert, Wouter van Joolingen

    Research output: Contribution to conferencePaper

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    Abstract

    This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students’ learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.
    Original languageEnglish
    Pages45-49
    Publication statusPublished - 29 Jun 2010
    Event9th International Conference of the Learning Sciences, ICLS 2010 - Chicago, United States
    Duration: 29 Jun 20102 Jul 2010
    Conference number: 9

    Conference

    Conference9th International Conference of the Learning Sciences, ICLS 2010
    Abbreviated titleICLS
    CountryUnited States
    CityChicago
    Period29/06/102/07/10

    Keywords

    • METIS-272034

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  • Cite this

    Baker, R. S. J. D., Gobert, J. D., & van Joolingen, W. (2010). Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors [discussant]. 45-49. Paper presented at 9th International Conference of the Learning Sciences, ICLS 2010, Chicago, United States.