Comparative Study of Data Analytics Readiness between ISEC and Non-ISEC Faculty
Keywords:
DELTTA, Big data analytics, Scholarly exchange curriculum, AcademicAbstract
Non-international scholarly exchange curriculum programs follow traditional educational frameworks may not help to refine educational processes. To overcome this shortcoming, this study determines the differences in data analytics readiness between international scholarly exchange curriculum and non-international scholarly exchange curriculum faculty in Chinese higher education institution. By using DELTTA instrument, the findings suggest that (i) expanding diverse samples, investigating multidimensional relationships, and leveraging mixed methods are key future directions, (ii) culturally responsive research will empower diverse faculty by uncovering nuanced insights to guide the development of inclusive big data analytics policies and environments in our increasingly data-driven educational landscape, and (iii) highlighting the need for targeted interventions to ensure uniform big data analytics readiness across diverse academic domains.
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References
Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.
Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Boston: Houghton Mifflin Company.
Carifio, J., & Perla, R. J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(3), 106-116.
Curry, E. (2016) The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. In J. Cavanillas, E. Curry, & W. Wahlster (Eds.), New horizons for a data-driven economy (pp. 29-37). Cham: Springer.
Daniel, B. K. (2017). Big data in higher education: The big picture. In B. K. Daniel (Ed.), Big data and learning analytics in higher education: Current theory and practice. Cham: Springer.
Davenport, T. H., Harris, J., & Morison, R. (2010). Analytics at work: Smarter decisions, better results. Boston: Harvard Business Press.
Gibson, D. (2017). Big data in higher education: Research methods and analytics supporting the learning journey. Technology, Knowledge and Learning, 22, 237-241.
Kim, H.Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52-54.
Król, K., & Zdonek, D. (2020). Analytics maturity models: An overview. Information, 11(3), 142.
Li, X., Zhang, W., Sun, Y., Chen, J., & Zhang, H. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.
Norman, G. (2010). Likert scales, levels of measurement and the "laws" of statistics. Advances in Health Sciences Education, 15, 625-632.
Proudfoot, D. E., Green, M., Otter, J. W., & Cook, D. L. (2018). STEM certification in Georgia's schools: A causal comparative study using the Georgia student growth model. Georgia Educational Researcher, 15(1), 16-39.
Sadler, G. R., Lee, H. C., Lim, R. S. H., & Fullerton, J. (2010). Recruiting hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nursing & Health Sciences, 12(3), 369-374.
Selwyn, N. (2015). Data entry: towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64-82.
Tasmin, R., & Huey, T. L. (2020). Determinants of big data adoption for higher education institutions in Malaysia. Research in Management of Technology and Business, 1(1), 254-263.
UNESCO. (2018). Global education monitoring report, 2019: Migration, displacement and education: Building bridges, not walls. Paris: UNESCO Publishing.
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