Reimaging Research Methodology as Data Science
AbstractThe growing volume of data generated by machines, humans, software applications, sensors and networks, together with the associated complexity of the research environment, requires immediate pedagogical innovations in academic programs on research methodology. This article draws insights from a large-scale research project examining current conceptions and practices of academics (n = 144) involved in the teaching of research methods in research-intensive universities in 17 countries. The data was obtained through an online questionnaire. The main findings reveal that a large number of academics involved in the teaching of research methods courses tend to teach the same classes for many years, in the same way, despite the changing nature of data, and complexity of the environment in which research is conducted. Furthermore, those involved in the teaching of research methods courses are predominantly volunteer academics, who tend to view the subject only as an “add-on” to their other teaching duties. It was also noted that universities mainly approach the teaching of research methods courses as a “service” to students and departments, not part of the core curriculum. To deal with the growing changes in data structures, and technology driven research environment, the study recommends institutions to reimage research methodology programs to enable students to develop appropriate competences to deal with the challenges of working with complex and large amounts of data and associated analytics. View Full-Text
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Daniel, B.K. Reimaging Research Methodology as Data Science. Big Data Cogn. Comput. 2018, 2, 4.
Daniel BK. Reimaging Research Methodology as Data Science. Big Data and Cognitive Computing. 2018; 2(1):4.Chicago/Turabian Style
Daniel, Ben K. 2018. "Reimaging Research Methodology as Data Science." Big Data Cogn. Comput. 2, no. 1: 4.
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