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Journals / Mühendislik Bilimleri ve Araştırmaları Dergisi / 2026 / Cilt: 8 Sayı: 2

Early Prediction of Academic Success in Higher Education: Optimized Machine Learning Models with Artificial Bee Colonies

Yükseköğretimde Akademik Başarı Durumunun Erken Tahmini: Yapay Arı Kolonisi ile Optimize Edilmiş Makine Öğrenmesi Modelleri

Mesut Ulu(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0002-5591-8674
Pages
221–232
Publication Date
2026-10-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

ABC optimization increased the performance of both models; ABC-SVM showed the highest overall success with 0.907 accuracy and 0.941 ROC-AUC.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, Support Vector Machines (SVM) and Extra Trees (ET) models were optimized with the Artificial Bee Colony (ABC) algorithm for early prediction of graduation and dropout status of students in higher education.
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Key Findings & Data:Analyzes were conducted on 3,630 students using 10 accessible variables at the end of the first semester.
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Conclusion & Impact:ABC optimization increased the performance of both models; ABC-SVM showed the highest overall success with 0.907 accuracy and 0.941 ROC-AUC.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Akademik başarı tahmini · Hiperparametre optimizasyonu · Destek Vektör Makineleri

Abstract

In this study, Support Vector Machines (SVM) and Extra Trees (ET) models were optimized with the Artificial Bee Colony (ABC) algorithm for early prediction of graduation and dropout status of students in higher education. Analyzes were conducted on 3,630 students using 10 accessible variables at the end of the first semester. Models were evaluated with multiple performance measures in an independent test set. ABC optimization increased the performance of both models; ABC-SVM showed the highest overall success with 0.907 accuracy and 0.941 ROC-AUC. ABC-ET, on the other hand, provided limited superiority in dropout class sensitivity. Permutation analysis showed that the number of courses successfully completed in the first semester was the most decisive variable. Findings reveal that ABC-based optimization offers an effective approach to early academic risk prediction.

Özet

Bu çalışmada, yükseköğretimde öğrencilerin mezuniyet ve terk durumlarının erken tahmini için Destek Vektör Makineleri (SVM) ve Ekstra Ağaçlar (ET) modelleri Yapay Arı Kolonisi (ABC) algoritmasıyla optimize edilmiştir. Analizler, birinci dönem sonunda erişilebilir 10 değişken kullanılarak 3.630 öğrenci üzerinde yürütülmüştür. Modeller bağımsız test kümesinde çoklu performans ölçütleriyle değerlendirilmiştir. ABC optimizasyonu her iki modelin performansını artırmış; ABC-SVM 0,907 doğruluk ve 0,941 ROC-AUC ile en yüksek genel başarıyı göstermiştir. ABC-ET ise terk sınıfı duyarlılığında sınırlı üstünlük sağlamıştır. Permütasyon analizi, birinci dönemde başarıyla tamamlanan ders sayısının en belirleyici değişken olduğunu göstermiştir. Bulgular, ABC tabanlı optimizasyonun erken akademik risk tahmininde etkili bir yaklaşım sunduğunu ortaya koymaktadır.

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Cite This Article

Ulu, M. (2026). Early Prediction of Academic Success in Higher Education: Optimized Machine Learning Models with Artificial Bee Colonies. Mühendislik Bilimleri ve Araştırmaları Dergisi, Cilt: 8 Sayı: 2, 221–232. https://doi.org/10.46387/bjesr.2019872

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