Mehmet Taha ESER, Cem Oktay GÜZELLER, Gökhan AKSU

PISA 2012 Sonuçlarının Duyuşsal Özelliklere Göre Kümeleme Çalışması

Bu çalışmanın amacı PISA 2012 öğrenci anketinde yer alan öz yeterlik, ilgi ve tutum ortalama puanlarına göre sınava katılan OECD üyesi ve diğer katılımcı ülkelerin tamamının nasıl kümelendiğinin belirlemesidir. Genel tarama modeline uygun olan çalışmada öz yeterlik ve ilgi puanlarına göre 68 ülke analiz edilmesine rağmen 25 ülkede kayıp veri sorunu olması nedeniyle tutum puanlarına göre 43 ülke analiz kapsamına alınmıştır. Çalışmada oluşacak kümelerin nasıl birleşeceğine ya da ayrışacağına karar vermeye çalışıldığından hiyerarşik kümeleme yaklaşımlarından tam bağlantı yöntemi kullanılmıştır. Analiz sonucunda elde edilen küme sayılarının geçerliğine ilişkin kanıt sunmak amacıyla k-ortalama ve diskriminant analizi yöntemi kullanılmıştır. Çalışma sonucunda öz yeterlik puanlarına göre 8, ilgi puanlarına göre 7 ve tutum puanlarına göre 6 farklı küme oluştuğu belirlenmiştir. Kümele sonuçları incelendiğinde öz yeterlik puanlarına göre Şangay ile Japonya'nın tek başına birer küme; ilgi puanlarına göre Romanya'nın tek başına bir küme; tutum puanlarına göre Danimarka ve Norveç ile Japonya ve Kore'nin tek başına birer küme oluşturduğu belirlenmiştir. Çalışmada ayrıca tek başına küme oluşturma eğiliminde olan ülkelerin çok boyutlu ölçekleme yöntemiyle elde edilen 2 boyutlu grafikte konum olarak diğer ülkelerden belirgin bir şekilde farklılık gösterdiği sonucuna ulaşılmıştır

Clustering Study of PISA 2012 Results According to Affective Attributes

In this research, it was determined that how all of the OECD member countries and other participant countries clustered according to their average self-efficacy, interest and attitude scores included in PISA 2012. The population of this research which is compatible with general screen model involves the OECD member countries and other participant countries attending PISA 2012 student questionnaire. Throughout the study, in order to determine the way in which clusters will unite or disperse full connection method was utilized through hierarchical clustering approaches. K-means and discriminate analysis methods were utilized in order to obtain evidence associated with the validity of the number of clusters obtained at the end of the analysis. Results conclude that 8 different clusters for self-efficacy scores, 7 different clusters for interest scores, and 6 different for attitude scores were obtained. Cluster analyses results yield that with the self-efficacy scores Shanghai and Japan formed one cluster for each; and with the attitude scores Denmark and Norway, Japan and Korea composed another for each. It can be seen that according to the self-efficacy scores, Turkey is in the same cluster with Australia, France, Chile, United Kingdom, Ireland, Belgium, Lithuania, New Zealand, Denmark, Sweden, Norway. According to this result, it can be seen that the countries tending to form a cluster individually in the cluster analysis can easily be distinguished visually thanks to the multi-dimensional scaling method however this method cannot be succeeded in differentiating cluster elements as long as the number of the cluster increase

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