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
DOI: 10.4018/978-1-7998-0249-5.ch005
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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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Data mining has been used in different areas such as Marketing, Banking, Insurance, Telecommunication, Health, Industry, Internet, Science and Engineering, and provided significant improvements in these areas. Recently, one of these areas is the educational environment. Educational data mining (EDM) literature has shown that it can represent new and significant contributions to educational research. Recently, various data mining methods have been implemented to different educational environment such as traditional, e-learning, computer-based learning etc. In this context, this study aimed to illustrate both uses of data mining methods and to present a study that implemented data mining methods in traditional education. By using data mining techniques, Aksoy, Narli, and Aksoy (2018) aimed to examine mathematically gifted students in terms of their learning styles, multiple intelligences, personality types, genders and grade levels in order to help teachers and educators to determine potential gifted students. Educational data mining literature and a short review of the characteristics included in the study will be described in next sections.

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