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

Öz

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.

Anahtar Kelimeler

• Kariyer Karar Sürecine Destek
• Eğitsel Veri Analizi
• Lojistik Regresyon
• Diskriminant Analiz

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

Öz

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.

Anahtar Kelimeler

• Career Decision Support
• Educational Data Analysis
• Discriminant Analysis
• Logistic Regression

Kaynakça

1. Alamgir, Z., Akram, H., Karim, S., & Wali, A. (2024). Enhancing student performance prediction via educational data mining on academic data. Informatics in Education, 23(1), 1-24.
2. Almgerbi, M., De Mauro, A., Kahlawi, A., & Poggioni, V. (2022). A systematic review of data analytics job requirements and online-courses. Journal of Computer Information Systems, 62(2), 422-434.
3. Arfaee, M., Bahari, A., & Khalilzadeh, M. (2022). A novel prediction model for educational planning of human resources with data mining approach: a national tax administration case study. Education and Information Technologies, 27(2), 2209-2239.
4. Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007
5. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of educational data mining, 1(1), 3-17.
6. Bayardalai, U., & Lee, J. H. (2025). Predicting Employment Status and Types of University Graduates in South Korea Using Machine-Learning Techniques. Industrial Engineering & Management Systems, 24(2), 265-281.
7. Bilal, M., Omar, M., Anwar, W., Bokhari, R. H., & Choi, G. S. (2025). Bridging the gap: from traditional admissions to data-driven insights for predicting and supporting undergraduate performance. Education and Information Technologies, 1-26.
8. Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521. https://doi.org/10.1016/j.eswa.2015.02.052

9. Diekuu, J. B., Mekala, M. S., Abonie, U. S., Isaacs, J., & Elyan, E. (2025). Predicting student next-term performance in degree programs using AI-based approach: a case study from Ghana. Cogent Education, 12(1), 2481000.
10. Gareth, J., Daniela, W., Trevor, H., & Robert, T. (2013). An introduction to statistical learning: with applications in R. Spinger. Golding, P., & Donaldson, O. (2006). Predicting academic performance. Proceedings - Frontiers in Education Conference, FIE, 21–26. https://doi.org/10.1109/FIE.2006.322661
11. Han, J., Kamber, M., & Mining, D. (2006). Concepts and techniques. Morgan kaufmann, 340, 94104-3205.
12. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
13. Hu, L. (2024). Analysis of employment information of university graduates through data mining. Automatic Control and Computer Sciences, 58(1), 58-65.
14. Imran, M., Latif, S., Mehmood, D., & Shah, M. S. (2019). Student academic performance prediction using supervised learning techniques. International Journal of Emerging Technologies in Learning, 14(14).
15. Kabakchieva, D., Stefanova, K., & Kisimov, V. (2011). Analyzing university data for determining student profiles and predicting performance. EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining, 347–348.
16. Kabakchieva, Dorina. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72. https://doi.org/10.2478/cait-2013-0006
17. Mezhoudi, N., Alghamdi, R., Aljunaid, R., Krichna, G., & Düştegör, D. (2021). Employability prediction: a survey of current approaches, research challenges and applications. Journal of Ambient Intelligence and Humanized Computing, 1-17.
18. Mohd Zaki, S., Razali, S., Awang Kader, M. A. R., Laton, M. Z., Ishak, M., & Mohd Burhan, N. (2025). Predicting students’ performance at higher education institutions using a machine learning approach. Kybernetes, 54(11), 6940-6975.
19. Nghe, N. T., Janecek, P., & Haddaway, P. (2007). A Comparative Analysis of Techniques for Predicting Academic Performance. In Proceedings - Frontiers in Education Conference, FIE. https://doi.org/https://doi.org/10.1109/FIE.2007.4417993
20. Nouib, H., Qadech, H., Benatiya Andaloussi, M., Chowdhury, S. J., & Moumen, A. (2025). Predicting Graduate Employability Using Hybrid AHP-TOPSIS and Machine Learning: A Moroccan Case Study. Technologies, 13(9), 385.
21. Oskouei, R. J., & Askari, M. (2014). Predicting Academic Performance with Applying Data Mining Techniques (Generalizing the results of two Different Case Studies). Computer Engineering and Applications Journal, 3(2), 79–88. https://doi.org/10.18495/comengapp.v3i2.81
22. Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72(February 2016), 414–422. https://doi.org/10.1016/j.procs.2015.12.157
23. Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., & Abreu, R. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance. Proceedings of the 8th International Conference on Educational Data Mining, 392–395.
24. http://www.educationaldatamining.org/EDM2015/proceedings/short392-395.pdf Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
25. Yehuala, M. A. (2015). Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University. International Journal of Scientific & Technology Research, 4(4), 91–94.
26. Yeung, C. K., & Yeung, D. Y. (2019). Incorporating features learned by an enhanced deep knowledge tracing model for stem/non-stem job prediction. International Journal of Artificial Intelligence in Education, 29(3), 317-341.
27. Zimmermann, J., Brodersen, K. H., Heinimann, H. R., & Buhmann, J. M. (2015). A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. JEDM - Journal of Educational Data Mining, 7(3), 151–176. http://www.educationaldatamining.org/JEDM/index.php/JEDM/article/view/JEDM070/pdf_19