PREDICTING ACADEMIC ACHIEVEMENT USING DATA MINING METHODS

Abstract

This study proposes a new model to analyze the grade point averages (GPAs) in the previous semester using data mining algorithms and to predict the final GPAs that students may receive in the following semesters in three gradually expanding categories (department, faculty, and university). The performances of the Random Forest, Linear Regression, Support Vector Machines, and k-Nearest Neighbors algorithms, which are among the data mining algorithms, were calculated and compared to estimate the GPAs of the students at the end of the semester. All algorithms applied correctly classified the samples at rates varying between 92% and 94%. The proposed model correctly estimated students’ grade point averages at the end of the semester with an average deviation of 0.28 points over a 4 with a single variable. Students with a high risk of failure can be determined in advance by estimating their final grade point averages.

Keywords

• Data Mining
• Machine Learning
• Predict Of Academic Performance
• Educational Data Mining
• Early Warning Systems

AKADEMİK BAŞARININ VERİ MADENCİLİĞİ YÖNTEMLERİYLE TAHMİN EDİLMESİ

Abstract

Bu çalışmada, öğrencilerin bir önceki döneme ait dönem sonu not ortalamalarını veri madenciliği yöntemleri ile analiz ederek sonraki dönemlerde alabileceği dönem sonu not ortalamalarını giderek genişleyen 3 kategoride (Bölüm, Fakülte, Üniversite bazında) tahmin edecek yeni bir model önerilmiştir. Veri seti, Türkiye’de bir Devlet Üniversitesindeki tüm öğrenci kayıtlarının tutulduğu Öğrenci Bilgi Sisteminden (ÖBS) alınmıştır. Veriler, Sınıf öğretmenliği bölümünden 426, Eğitim fakültesinden 2.379 ve Üniversite genelinde eğitim gören 5.149 öğrencinin 2017-2018 Güz ve Bahar Yarıyılı dönem sonu not ortalamalarını içermektedir. Öğrencilerin dönem sonundaki genel not ortalamalarını tahmin etmek için veri madenciliği algoritmalarından rastgele orman, lineer regresyon, destek vektör makineleri ve k-en yakın komşular algoritmalarının başarımı hesaplanmış ve karşılaştırılmıştır. Uygulanan tüm algoritmalar örnekleri %92 ile %94 arasında değişen oranlarda doğru bir şekilde sınıflandırmıştır. Önerilen model, öğrencilerin dönem sonu not ortalamalarını tek bir değişken ile 4 üzerinden 0,28 puanlık ortalama sapma ile doğru tahmin etmiştir. Dönem sonu not ortalamalarının tahmin edilmesi sayesinde başarısız olma riski yüksek olan öğrenciler önceden belirlenebilir.

Keywords

• Veri madenciliği
• Makine öğrenmesi
• Eğitsel Veri Madenciliği
• Akademik performansın tahmin edilmesi
• Erken Uyarı Sistemleri

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