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• W2328991232 abstract "One of the general discussion in the studies about learning style is what degree of students whose learning style is determined, have other learning styles. In this context, the aim of this study is to determine the learning styles of prospective elementary mathematics teachers and to explore the relationships between these styles by using data mining techniques. Data mining can be defined as applications of different algorithms to identify patterns and relationships in a data set. For this purpose, Grasha-Reichmann Learning Styles Inventory was applied to 400 prospective elementary mathematics teachers at Dokuz Eylul University. Cronbach's alpha reliability coefficient of the scale was found as 0.83.Results show that more than 50% of female students have independent’’ learning style. At the same time students who have competitive learning style had the least number of students. The male students who have collaborative and dependent learning styles were the majority.. From Class 1 to Class 4, it was observed that the number of students who have individual learning styles was decreasing and the number of students who have cooperative learning styles was increasing. In network graph, it was found that one of the strongest relationships was between the students who have cooperative and independent learning style with high level. On the other hand the relationship between the students who have passive and independent learning style with low level was not seen in graph. The decision tree indicates that the most effective attribute is independent learning style to identify which level of the learning style students have. Besides in the Data mining, learning styles, Mathematics Education association rules model several rules are constructed with %75 confidence." @default.
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• W2328991232 date "2014-06-01" @default.
• W2328991232 modified "2023-09-26" @default.
• W2328991232 title "Investigation of Prospective Elementary Mathematics Teachers’ Learning Styles and Relationships between Them Using Data Mining" @default.
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• W2328991232 doi "https://doi.org/10.17278/ijesim.2014.01.004" @default.
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