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Educational Data Mining Techniques and Applications

Educational Data Mining Techniques and Applications

ISBN13: 9781799871033|ISBN10: 1799871037|ISBN13 Softcover: 9781799885658|EISBN13: 9781799871040
DOI: 10.4018/978-1-7998-7103-3.ch011
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MLA

Govindarajan, M. "Educational Data Mining Techniques and Applications." Advancing the Power of Learning Analytics and Big Data in Education, edited by Ana Azevedo, et al., IGI Global, 2021, pp. 234-251. https://doi.org/10.4018/978-1-7998-7103-3.ch011

APA

Govindarajan, M. (2021). Educational Data Mining Techniques and Applications. In A. Azevedo, J. Azevedo, J. Onohuome Uhomoibhi, & E. Ossiannilsson (Eds.), Advancing the Power of Learning Analytics and Big Data in Education (pp. 234-251). IGI Global. https://doi.org/10.4018/978-1-7998-7103-3.ch011

Chicago

Govindarajan, M. "Educational Data Mining Techniques and Applications." In Advancing the Power of Learning Analytics and Big Data in Education, edited by Ana Azevedo, et al., 234-251. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7103-3.ch011

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Abstract

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

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