ÖĞRENCİLERİN MATEMATİK BAŞARILARINI ETKİLEYEN FAKTÖRLERİN RASTGELE ORMAN,  ÇOK KATMANLI ALGILAYICI VE RADYAL TABANLI FONKSİYON YAPAY SİNİR AĞLARI YÖNTEMLERİ İLE TAHMİNLEME YETENEĞİ AÇISINDAN KARŞILAŞTIRILMASI: Türkiye Örneği

Özlem  BEZEK GÜRE

ÖĞRENCİLERİN MATEMATİK BAŞARILARINI ETKİLEYEN FAKTÖRLERİN RASTGELE ORMAN, ÇOK KATMANLI ALGILAYICI VE RADYAL TABANLI FONKSİYON YAPAY SİNİR AĞLARI YÖNTEMLERİ İLE TAHMİNLEME YETENEĞİ AÇISINDAN KARŞILAŞTIRILMASI: Türkiye Örneği

Thesis Type: Doctorate

Institution Of The Thesis: Van Yüzüncü Yıl Üniversitesi, Fen Bilimleri Enstitüsü, İstatistik (Dr), Turkey

Approval Date: 2019

Thesis Language: Turkish

Student: Özlem BEZEK GÜRE

Principal Supervisor (For Co-Supervisor Theses): Fevzi Erdoğan

Co-Supervisor: Murat Kayri

Open Archive Collection: AVESIS Open Access Collection

Abstract:

In this dissertation study, in order to determine the factors affecting the Mathematics literacy of students participating in PISA 2015 test in Turkey, the methods of Random Forest from Decision Trees and Multilayer Perceptron Artificial Neural Networks (MLPANN) and Radial Basis Function Artificial Neural Networks (RBFANN) from Artificial Neural Networks (ANN) were implemented according to PISA 2015 data. The information, which was collected from a total of 4422 students, 2165 (49%)of whom were boys and 2257 (51%) of whom were girls having participated in PISA 2015 test, was used. The scores, which the students, having gone in for PISA 2015 test, got from mathematics test were included in the model as dependent variables; and 25 variables, which were thought to have connection with dependent variables institutionally, were included in the model as independent variables. In the study, three methods were compared in terms of predictability; and when the model was tested, it was observed that Random Forest method estimated the model with fewer errors in terms of a number of performance indicators compared to the other methods. Therefore, it is possible to say that Random Forest is more consistent in terms of performance criteria; and its estimating ability is a little higher compared to MLPANN and RBFANN. Moreover, when MLPANN and RBFANN methods are compared, it is concluded that MLPANN exhibited a better performance compared to RBFANN; on the other hand, when MLPANN and RF were compared, it was found out that RF was more successful.