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Journals / Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi / 2026 / Cilt: 9 Sayı: 1

PREDICTION WITH MACHINE LEARNING ALGORITHMS FOR COMPUTER ENGINEERING DEPARTMENT AND EVALUATION WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE

BİLGİSAYAR BÖLÜMÜ ÖĞRENCİLERİ İÇİN MAKİNE ÖĞRENME ALGORİTMALARI İLE KARİYER ÖNERİSİ VE AÇIKLANABİLİR YAPAY ZEKA İLE DEĞERLENDİRİLMESİ

Emine Altunbas(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ TEKNOLOJİ FAKÜLTESİ)0009-0007-8262-7796
Cevriye Altıntaş(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0001-5928-3402
Pages
133–149
Publication Date
2025-06-30
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AI Summary & Key Takeaways

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

The results show that the proposed method can be used in career guidance and educational planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it is aimed to predict which professional field computer engineering students can work in according to their technical skills and academic performance using machine learning algorithms and to evaluate these predictions using explainable artificial intelligence (AI) techniques.
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Key Findings & Data:The original dataset consisting of 174 students includes variables such as general grade point average, programming language proficiency, project information and internship areas.
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Conclusion & Impact:The results show that the proposed method can be used in career guidance and educational planning.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sınıflandırma · LIME · SHAP

Abstract

In this study, it is aimed to predict which professional field computer engineering students can work in according to their technical skills and academic performance using machine learning algorithms and to evaluate these predictions using explainable artificial intelligence (AI) techniques. The original dataset consisting of 174 students includes variables such as general grade point average, programming language proficiency, project information and internship areas. Eight different classification algorithms were compared with LazyClassifier and the most successful models were trained with hyperparameter optimization. The highest accuracy was obtained as 97% with the Gradient Boosting algorithm. The model's decisions were interpreted with SHAP and LIME algorithms and the explainability of the model was ensured. The results show that the proposed method can be used in career guidance and educational planning.

Özet

Bu çalışmada, bilgisayar mühendisliği öğrencilerinin teknik becerilerine ve akademik performanslarına göre hangi mesleki alanda çalışabileceklerinin makine öğrenme algoritmaları ile tahmin edilmesi ve bu tahminlerin açıklanabilir yapay zekâ (AYZ) teknikleriyle değerlendirilmesi amaçlanmıştır. 174 öğrenciden oluşan özgün veri kümesinde, genel not ortalaması, programlama dili yeterlilikleri, proje bilgileri ve staj alanları yer almaktadır. AdaBoost, Decision Tree, Gradient Boosting, KNN, Logistic Regression, Naive Bayes, Random Forest, SVC algoritmaları LazyClassifier ile kıyaslanmış ve en başarılı Gradient Boosting ve Decision Tree modelleri hiperparametre optimizasyonu ile eğitilmiştir. En yüksek doğruluk Gradient Boosting algoritması ile %97 olarak elde edilmiştir. Modelin kararları SHAP ve LIME algoritmalarıyla yorumlanarak modelin açıklanabilirliği sağlanmıştır. Sonuçlar, önerilen yöntemin kariyer yönlendirme ve eğitim planlamasında kullanılabilir olduğunu göstermektedir.

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Cite This Article

Altunbas, E., Altıntaş, C. (2025). PREDICTION WITH MACHINE LEARNING ALGORITHMS FOR COMPUTER ENGINEERING DEPARTMENT AND EVALUATION WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi, Cilt: 9 Sayı: 1, 133–149. https://doi.org/10.62301/usmtd.1720980

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