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    Preparing the Next Generation of Education Researchers for Big Data in Higher Education

    Access Status
    Fulltext not available
    Authors
    Gibson, David
    Ifenthaler, Dirk
    Date
    2017
    Type
    Book Chapter
    
    Metadata
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    Citation
    Gibson, D. and Ifenthaler, D. 2016. Preparing the Next Generation of Education Researchers for Big Data in Higher Education, in Kei, D.B. (ed), Big Data and Learning Analytics in Higher Education, pp. 29-42. Cham: Springer.
    Source Title
    Big Data and Learning Analytics in Higher Education
    DOI
    10.1007/978-3-319-06520-5_4
    ISBN
    9783319065205
    Faculty
    Curtin Learning and Teaching (CLT)
    School
    Learning Futures
    URI
    http://hdl.handle.net/20.500.11937/75930
    Collection
    • Curtin Research Publications
    Abstract

    Research in social science, education, psychology, and humanities is still dominated by research methodologies that primarily divide the world into either qualitative or quantitative approaches. This relatively small toolkit for understanding complex phenomena in the world limits the next generation of education researchers when they are faced with the increased availability of big data. In this chapter, we are calling attention to data mining, model-based methods, machine learning, and data science in general as a new toolkit for the next generation of education researchers and for the inclusion of these topics in researcher preparation programs. A review of the state of the art in research methodology courses and units shows that most follow a traditional approach focusing on quantitative and/or qualitative research methodologies. Therefore, this chapter makes a case for a new data science foundation for education research methodology. Finally, benefits and limitations of computationally intensive modeling approaches are critically reviewed.

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