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Journals / Journal of Turkish Operations Management / 2025 / Cilt: 9 Sayı: 2

Data-driven prediction of career trajectories of industrial engineering students using performance metrics and classification techniques

Endüstri mühendisliği öğrencilerinin kariyer yollarının, performans metrikleri ve sınıflandırma teknikleriyle veri temelli olarak tahmin edilmesi

Fatma Yerlikaya Özkurt(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0002-4747-8461
Ayşe Kuyrukçu(ATILIM ÜNİVERSİTESİ)0009-0003-9067-3267
Pages
404–423
Publication Date
2025-12-30
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AI Summary & Key Takeaways

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

Additionally, discriminant analysis proves effective in categorizing data in relation to industry sectors and occupational status.

📌 3 Key Takeaways

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Objective & Hypothesis:The increasing diversity of professions and the multitude of career options have made the process of job selection more challenging and crucial.
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Key Findings & Data:The results, as evaluated by accuracy metrics and classification performance measures, reveal higher rates of correct classification when considering occupational status as the dataset's response variable.
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Conclusion & Impact:Additionally, discriminant analysis proves effective in categorizing data in relation to industry sectors and occupational status.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kariyer Karar Sürecine Destek · Eğitsel Veri Analizi · Lojistik Regresyon

Abstract

The increasing diversity of professions and the multitude of career options have made the process of job selection more challenging and crucial. For aspiring industrial engineers, this choice is particularly complex due to their interdisciplinary education. Their curriculum covers a diverse set of engineering and business courses, including production, modeling, optimization, database, economics, and project management. Unlike some other engineering disciplines, industrial engineering lacks a specific job area definition. This unique situation led to the selection of IE students and graduates as subjects for this study. The study focuses on the mandatory departmental courses for IE students and their corresponding grades. A sample group comprises graduates currently employed in various fields. The primary objective is to establish a relationship between students'coursework and their current job positions through data mining techniques, specifically discriminant analysis and logistic regression. The results, as evaluated by accuracy metrics and classification performance measures, reveal higher rates of correct classification when considering occupational status as the dataset's response variable. Additionally, discriminant analysis proves effective in categorizing data in relation to industry sectors and occupational status.

Özet

Mesleklerin giderek çeşitlenmesi ve kariyer seçeneklerinin artması, iş seçme sürecini hem daha karmaşık hem de daha kritik hâle getirmiştir. Endüstri mühendisi adayları için bu süreç, disiplinlerarası eğitim yapılarından ötürü özellikle karmaşıktır. Müfredatları; üretim, modelleme, optimizasyon, veri tabanı, ekonomi ve proje yönetimi gibi mühendislik ve işletme alanlarını kapsayan geniş bir ders yelpazesinden oluşmaktadır. Diğer bazı mühendislik dallarından farklı olarak, endüstri mühendisliğinin belirgin bir meslek alanı tanımı bulunmamaktadır. Bu özgün durum, çalışmada örneklem olarak endüstri mühendisliği öğrencileri ve mezunlarının seçilmesine neden olmuştur. Bu çalışma, endüstri mühendisliği öğrencilerine yönelik zorunlu bölüm dersleri ve bu derslerden aldıkları notlar üzerine odaklanmaktadır. Örneklem grubunu, farklı sektörlerde istihdam edilen mezunlar oluşturmaktadır. Çalışmanın temel amacı, öğrencilerin aldıkları derslerle mevcut iş pozisyonları arasındaki ilişkiyi veri madenciliği teknikleri aracılığıyla ortaya koymaktır. Bu amaç doğrultusunda diskriminant analizi ve lojistik regresyon yöntemleri kullanılmıştır. Doğruluk metrikleri ve sınıflandırma performans ölçütlerine göre değerlendirilen sonuçlar, iş pozisyonu değişkeni bağımlı değişken olarak ele alındığında, doğru sınıflandırma oranlarının yüksek olduğunu göstermektedir. Ayrıca, diskriminant analizi yöntemi, hem sektör bazında hem de mesleki pozisyonlara göre verinin kategorize edilmesinde etkili bir araç olarak öne çıkmaktadır.

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Cite This Article

Özkurt, F. Y., Kuyrukçu, A. (2025). Data-driven prediction of career trajectories of industrial engineering students using performance metrics and classification techniques. Journal of Turkish Operations Management, Cilt: 9 Sayı: 2, 404–423. https://doi.org/10.56554/jtom.1745631

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