Yükseköğretimde Öğrenci Başarılarının Sınıflandırılmasında Yapay Sinir Ağları ve Lojistik Regresyon Yöntemlerinin Kullanılması

Öğrenci Başarılarının Yapay Sinir Ağları ile Kestirilmesi

“…Günümüzün önemli ve yaygın kullanılan uygulamaları arasında kabul edilen (Deperlioğlu & Köse, 2011;Elmas, 2003, 22;Öztemel, 2003, 13) yapay zekâ bilimi ve özellikle YSA'nın; sınıflama, modelleme, kestirim (tahmin), optimizasyon gibi önemli alanlarda geçerli ve başarılı sonuçlar elde ettiği yapılan çalışmalarla (Toprak, 2017;Tezbaşaran, 2016;Bahadır, 2016;Kuzmanovic, Jevric, Gajic, Kovacevic, Vasiljevic, Kecojevic & İvanovic, 2015;Bou-Rabee, Suliaman, Choe, Han, Saaed & Marati, 2015;Tekin, 2014;Şevik, Aktaş, Özdemir ve Doğan, 2014;Kasaplı, 2014;Rahmani & Aprilianto, 2014;Turhan vd., 2013;Musso, Kyndt, Cascallar & Dochy, 2013;Ötkün ve Karlık, 2013;Ataseven, 2013;Koç, 2012;Çırak, 2012;Tepehan, 2011;Açıkbaş, Kaypmaz ve Söylemez, 2010;Lee, 2010;Burmaoğlu, 2009;Helhel, 2009;Asilkan ve Irmak, 2009;Oladokun vd., 2008;Aslan, 2008;Yılmaz, Güneş ve Aksu, 2007;Caner ve Üstün, 2006;Ocakoğlu, 2006;Erdem ve Uzun, 2005;Naik & Rogathaman, 2004;Çikoğlu, Temurtaş ve Yumurcak, 2004) research to be conducted by selecting specific cases that have specific characteristics and which carry rich information of the desired information depending on the purpose of the study (Büyüköztürk et al, 2014, 90).…”

Estimating of student success with artificial neural networks

“…Predict one additional new student's success in future for model, possibility of prediction is determined as 74% with ANN. In a study, multilayered artificial neural network model's correct classification rate was obtained as 70.16% (Cirak & Cokluk, 2013).…”

“…are employed. Lately, the most preferred analysis technique has been artificial neural networks (Cirak & Cokluk, 2013;Kim, 2010).…”

“…In the artificial neural network technique, the total input value is multiplied by its weight collected value and processed with the activation function to obtain output (Kumar, 2004;Shanmuganathan & Samarasinghe, 2016). Despite the limited number of its applications in education, the artificial neural networks method is used successfully in many areas such as transport and aviation, biomedical and pharmaceutical industries, finance, stock market, and credit card applications (Cirak & Cokluk, 2013).…”

Comparison of Data Mining Classification Algorithms on Educational Data under Different Conditions

“…In classification studies in the field of education, the performance of methods such as decision trees, support vector machines, logistic regression, neural networks, Bayes algorithms, k-nearest neighborhood are examined and compared (e.g., Bahadır, 2013;Barker, Trafalis & Rhoads, 2004;Berens, Schneider, Gortz, Oster, & Burghoff, 2019;Çırak, 2012;Dekker, Pechenizkiy & Vleeshouwers, 2009;Göker, 2012;Hamalainen & Vinni, 2006;Hamalainen & Vinni, 2011;Minaei-Bidgoli, Kashy, Kortemeyer & Punch, 2003;Osmanbegović & Suljić, 2012;Romero, Espejo, Zafra, Romero & Ventura, 2013;Romero, Ventura, Espejo & Hervas, 2008;Shahiri, Husain & Rashid, 2015;Sweeney, Lester, Rangwala, & Johri 2016;Şengür, 2013;Tepehan, 2011;Tezbaşaran, 2016;Tosun, 2007;Yurdakul & Topal, 2015). In addition, methods were compared according to the different number of categories of the dependent variable (Minaei-Bidgoli, Kashy, Kortemeyer & Punch, 2003;Nghe, Janecek & Haddawy, 2007), the data structure (Romero et al, 2008;, amount of missing and noisy data ( Hamalainen & Vinni, 2011) and sample sizes (Hamalainen & Vinni, 2006;2011).…”