Examination of Mathematically Gifted Students Using Data Mining Techniques in terms of Some Variables

Abstract

In the identification process, there may be gifted students who may be unnoticed or students who are misdiagnosed and are disappointed. In this context, this study is a step that may solve these two problems about the identification of mathematically gifted students with the help of data mining, which is data analysis methodology that has been successfully used in different areas including education. The decision tree model is one data mining technique, which was implemented using students’ learning styles, multiple intelligences and personality types to identify gifted students. The sample size was 735 middle school students (234 mathematically gifted and 501 non-gifted) studying in two different cities in Turkey. 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.