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An Educational Data Mining Application by Using Multiple Intelligences

An Educational Data Mining Application by Using Multiple Intelligences

Esra Aksoy, Serkan Narli, Mehmet Akif Aksoy
Copyright: © 2020 |Pages: 18
ISBN13: 9781799802495|ISBN10: 1799802493|EISBN13: 9781799802518
DOI: 10.4018/978-1-7998-0249-5.ch005
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MLA

Aksoy, Esra, et al. "An Educational Data Mining Application by Using Multiple Intelligences." Examining Multiple Intelligences and Digital Technologies for Enhanced Learning Opportunities, edited by Robert Z. Zheng, IGI Global, 2020, pp. 93-110. https://doi.org/10.4018/978-1-7998-0249-5.ch005

APA

Aksoy, E., Narli, S., & Aksoy, M. A. (2020). An Educational Data Mining Application by Using Multiple Intelligences. In R. Zheng (Ed.), Examining Multiple Intelligences and Digital Technologies for Enhanced Learning Opportunities (pp. 93-110). IGI Global. https://doi.org/10.4018/978-1-7998-0249-5.ch005

Chicago

Aksoy, Esra, Serkan Narli, and Mehmet Akif Aksoy. "An Educational Data Mining Application by Using Multiple Intelligences." In Examining Multiple Intelligences and Digital Technologies for Enhanced Learning Opportunities, edited by Robert Z. Zheng, 93-110. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0249-5.ch005

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Abstract

The aim of this chapter is to illustrate both uses of data mining methods and the way of these methods can be applied in education by using students' multiple intelligences. Data mining is a data analysis methodology that has been successfully used in different areas including the educational domain. In this context, in this study, an application of EDM will be illustrated by using multiple intelligence and some other variables (e.g., learning styles and personality types). The decision tree model was implemented using students' learning styles, multiple intelligences, and personality types to identify gifted students. The sample size was 735 middle school students. The constructed decision tree model with 70% validity revealed that examination of mathematically gifted students using data mining techniques may be possible if specific characteristics are included.

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