The aim of this study is to examine middle school students" attitude towards mathematics in the context of their mathematic learning preferences using data mining which is data analysis methodology that has been successfully used in different areas including educational domains. "How do I actually learn?" questionnaire and attitude scale were applied to 702 middle school students studying in three different cities of Turkey. Demographic data (gender, grade level, parents" education level, pre-school education) were also gathered. Data mining techniques such as decision tree was implemented. Furthermore web graph was used for visualization of relationship between mathematic learning preference and attitude towards mathematics. Constructed decision tree models with C5.0 algorithm revealed that all of variables used in this study are related to the attitude towards mathematics but the most effective attribute is found as grade level. Using web graph, it was found that the strongest relationship was between reflective learning preference and positive level attitude towards mathematics.
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. Extended SummaryWith the transition to learner-centered education, approaches related to teaching and learning began to be questioned and developed in education system. The fundamental basis of the change is individual features of students and effect of these features to educational environment.Teachers can use individual differences in planning learning environments and processes (Tomlinson, 1997: 25). These individual differences consist of a lot of feature such as learning style. Kolb (1984) defines learning style as a preferred way of gathering information, whereas for Dunn (1984) learning style is an individual way of absorbing and retaining information or skills. Özer (1998) reported that teachers must know students' learning styles for effective learning.
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.
Bu çalışmada irrasyonel sayı kümesi ile rasyonel ve gerçek sayı kümelerinin ilişkilerine yönelik öğrencilerin öğrenme güçlüklerini araştırmak amaçlanmıştır. Bu amaçla açık uçlu sorulardan oluşan "İrrasyonel Sayı Kavram Testi' geliştirilmiştir. Geliştirilen veri toplama aracı 8.sınıfta öğrenim gören 58 öğrenciye ve 9.sınıfta öğrenim gören 50 öğrenciye uygulanmıştır. Farklı kademelerden maksimum çeşitlilik örneklemesi ile seçilen 5'er öğrenci ile yarı yapılandırılmış görüşme yapılmıştır. Öğrencilerin her iki kademede de gerçek sayı kümesi ile diğer sayı kümelerinin arasındaki ilişkiyi anlamada güçlük yaşadıkları görülmüştür. Öğrencilerde irrasyonel sayılarının tamamının gerçek sayı olamayabileceği düşüncesi ile bir sayının hem rasyonel hem de irrasyonel olabileceği düşüncesi mevcuttur. Bu yanlış düşüncelere sahip öğrenci oranının 8.sınıflarda 9.sınıflara göre daha fazla olduğu görülmüştür.
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