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

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...

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Main Authors: Gibson, David, Ifenthaler, Dirk
Format: Book Chapter
Published: Springer International Publishing 2017
Online Access:http://hdl.handle.net/20.500.11937/75930
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author Gibson, David
Ifenthaler, Dirk
author_facet Gibson, David
Ifenthaler, Dirk
author_sort Gibson, David
building Curtin Institutional Repository
collection Online Access
description 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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institution Curtin University Malaysia
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publishDate 2017
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spelling curtin-20.500.11937-759302019-07-09T01:08:20Z Preparing the Next Generation of Education Researchers for Big Data in Higher Education Gibson, David Ifenthaler, Dirk 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. 2017 Book Chapter http://hdl.handle.net/20.500.11937/75930 10.1007/978-3-319-06520-5_4 Springer International Publishing restricted
spellingShingle Gibson, David
Ifenthaler, Dirk
Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title_full Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title_fullStr Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title_full_unstemmed Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title_short Preparing the Next Generation of Education Researchers for Big Data in Higher Education
title_sort preparing the next generation of education researchers for big data in higher education
url http://hdl.handle.net/20.500.11937/75930