Önerilen Yapay Sinir Ağı Algoritması ile Ortaokul Öğrencilerin Akademik Performansının Tahmini

Eğitsel veri madenciliği, eğitim sürecine ilişkin elde edilen büyük veri üzerinde farklı kaynakları kullanarak şimdiki zamana ve gelecek zamana ilişkin tahmin yapmamızı sağlayacak kural ve ilişkileri araştırır. Eğitsel veri madenciliği ile veri madenciliği alanındaki teknik ve algoritmaların kullanılmasıyla öğrenci veya eğitmenlerin akademik performansları tahmin edilebilir. Bu çalışmada, orta okul öğrencilerinin akademik performansını tahmin etmek amacıyla yeni bir yapay sinir ağı algoritması önerilmektedir. Önerilen algoritma, öncelikli olarak dengesiz sınıf dağılımı problemini çözmek için aşırı örnekleme tekniklerinden SMOTE algoritmasını önişleme aşamasında uygulamaktadır. Daha sonra, öznitelik seçim ve veri normalizasyonu işlemleri yapılarak çalışılan öğrenci veri seti, önerilen algoritmanın kullanımına hazır hale getirilmektedir. Çalışmada kullanılan rastgele arama algoritması ile yapay sinir ağı modelinin hiper-parametreleri optimize edilmektedir. Öğrencilerin Matematik ve Portekizce derslerindeki başarıları 2-seviyeli ve 5-seviyeli sınıflandırma için önerilen algoritma ile tahmin edilmektedir. Deney sonuçlarında, Matematik dersi için %97.0 ve %92.3 doğruluk değerleri sırasıyla 2-seviyeli ve 5-seviyeli sınıflandırma için elde edilmektedir. Protekizce dersi için ise bu değerler sırasıyle %97.6 ve %87.9 olarak hesaplanmaktadır.

Anahtar Kelimeler:

Eğitsel Veri Madenciliği, Yapay Sinir Ağları, Rastgele Arama

Prediction of Secondary School Students' Academic Performance with Proposed Artificial Neural Networks Algorithm

Educational data mining explores the rules and relationships that will enable us to make predictions about the present and the future by using different sources on the big data obtained about the educational process. With educational data mining, students' or instructors’ academic performance is predicted by using various techniques and algorithms in data mining. In this study, a new artificial neural network algorithm is proposed to predict the academic performance of secondary school students. The proposed algorithm primarily applies the SMOTE algorithm, one of the oversampling techniques, in the preprocessing stage to solve the problem of unbalanced class distributions. Then, the student dataset is made ready for the use of the proposed algorithm by performing feature selection and data normalization processes. The hyper-parameters of the artificial neural network model are optimized by using the random search algorithm used in the study. Students' achievement in Mathematics and Portuguese lessons are predicted for 2-level and 5-level classification. In the experimental results, the 97.0% and 92.3% accuracy values for the Mathematics course are obtained for 2-level and 5-level classification, respectively by using the proposed algorithm. For the Portuguese course, these values are calculated as 97.6% and 87.9%, respectively.

Keywords:

Educational Data Mining, Artificial Neural Networks, Random Search,

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