Sevda AGHALAROVA, Sinem BOZKURT KESER

Öğrencilerin Akademik Performanslarının Tahmin Edilmesi için AutoML Tekniğinin Uygulanması

Eğitsel Veri Madenciliği, çeşitli eğitim kaynaklarından elde edilen büyük miktarda verinin analizini kolaylaştırmak amacıyla veri madenciliği yöntemlerinin geliştirilmesidir. Eğitimcilere geribildirimde bulunma, öğrencilere ders önerisinde bulunma, istenmeyen öğrenci davranışı belirleme, öğrenci performansını tahmin etme gibi konular Eğitsel Veri Madenciliği çalışma alanları arasında gösterilebilir. Doğru modeller oluşturularak bu alanlarda yapılacak iyileştirmeler ile eğitim kalitesi geliştirilebilir. Doğru modeller oluşturmak için uygun makine öğrenmesi algoritmalarının seçimi hem eğitimciler hem de veri bilimcileri için son derece önemlidir. Bu çalışmada öğrencilerin akademik performanslarını tahmin etmek amacıyla Otomatik Makine Öğrenmesi yöntemi ile çalışmada kullanılan veri kümesi için en iyi model araştırılmaktadır. Otomatik Makine Öğrenmesi ile veri önişleme, model seçimi ve hiper-parametre optimizasyonu gibi zorlu görevlerle uğraşmadan en iyi model bulunabilmektedir. Çalışmada, gerçek veri seti için Dağıtılmış Rastgele Orman algoritması en iyi algoritma olarak belirlenmektedir. Izgara araması kullanılarak algoritmanın hiper-parametreleri optimize edilmektedir. Deney sonuçlarında, Dağıtılmış Rastgele Orman algoritmasının, varsayılan hiper-parametreleri ile doğruluk ve f-skor değerleri sırasıyla %77.50 ve %80.01 olarak elde edilmektedir. Izgara araması ile bulunan optimal hiper-parametreler için doğruluk ve f-skor değerleri ise sırasıyla %82.30 ve %82.50 olarak hesaplanmaktadır.

Anahtar Kelimeler:

Eğitsel Veri Madenciliği, Makine Öğrenmesi, Öğrenci Akademik Performans Tahmini, Otomatik Makine Öğrenmesi.

Application of AutoML Technique for Predicting Academic Performance of Students

Educational Data Mining is the development of data mining methods to facilitate the analysis of large amounts of data obtained from various educational sources. Issues such as providing feedback to educators, suggesting courses to students, identifying undesirable student behavior, and predicting the academic performance of students can be shown among the fields of Educational Data Mining. The quality of education can be improved with the improvements to be made in these areas by creating the right models. The selection of suitable machine learning algorithms to build accurate models is highly important for educators and data scientists. In this study, the best model for the dataset used in the study is investigated with the Automatic Machine Learning method in order to predict the students' academic performance. The best model can be found without dealing with difficult tasks such as data preprocessing, model selection, and hyper-parameter optimization using Automatic Machine Learning. In the study, the Distributed Random Forest algorithm is determined as the best algorithm for the real-world data set. And, the hyper-parameters of the algorithm are optimized using grid search. In the results of the experiments, the default hyper-parameters of the Distributed Random Forest algorithm and the accuracy and f-score values were obtained as 77.50% and 80.01%, respectively. For the optimal hyper-parameters found by grid search, the accuracy and f-score values are calculated as 82.30% and 82.50%, respectively.

Keywords:

Educational Data Mining, Machine Learning, Prediction Student Academic Performance, AutoML.,

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https://docs.h2o.ai/, Erişim tarihi 23.04.2021.