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dc.contributor.authorGibson, David
dc.contributor.authorIfenthaler, Dirk
dc.identifier.citationGibson, 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.

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.

dc.publisherSpringer International Publishing
dc.titlePreparing the Next Generation of Education Researchers for Big Data in Higher Education
dc.typeBook Chapter
dcterms.source.titleBig Data and Learning Analytics in Higher Education
curtin.departmentLearning Futures
curtin.accessStatusFulltext not available
curtin.facultyCurtin Learning and Teaching (CLT)
curtin.contributor.orcidGibson, David [0000-0003-1053-4690]
curtin.contributor.scopusauthoridGibson, David [35731134600]

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