Dergiler / International Journal of Assessment Tools in Education / 2026 / Volume: 13 Issue: 3

Evaluating an education model via Pythagorean fuzzy AHP and decision tree

Sema Çiftçi(OSTİM TEKNİK ÜNİVERSİTESİ)0000-0003-4265-2384
Serpil Özer(OSTIM TECHNICAL UNIVERSITY)0000-0003-4075-773X
Sayfa
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Yayın Tarihi
2026-09-02
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This study makes a theoretical contribution to the literature on applied education by presenting a multi-criterion, data-driven analysis model that integrates student perspectives with expert evaluations to develop feasible suggestions for strengthening the alignment between higher education and industry.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Applied education models aim to prepare students for professional careers by bridging the gap between theoretical knowledge and practical experience.
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Temel Bulgular & Veri:The data were obtained from 76 students and 13 academic and industry experts.
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Sonuç & Katkı:This study makes a theoretical contribution to the literature on applied education by presenting a multi-criterion, data-driven analysis model that integrates student perspectives with expert evaluations to develop feasible suggestions for strengthening the alignment between higher education and industry.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Applied education · Fuzzy-AHP · Decision trees

Abstract

Applied education models aim to prepare students for professional careers by bridging the gap between theoretical knowledge and practical experience. However, existing literature on these models has mostly focused on expert and institutional perspectives, with limited consideration given to students' perceptions and expectations. This study examines an applied education model implemented at an industrial university. Taking a holistic approach, the study evaluates student feedback and the opinions of academic and industry experts to fill this gap in the literature. The research used a hybrid methodological framework combining the Pythagorean Fuzzy Analytic Hierarchy Process and a decision tree algorithm. The data were obtained from 76 students and 13 academic and industry experts. The Pythagorean Fuzzy Analytic Hierarchy Process was used to determine the weights of the evaluation criteria based on the opinions of students and experts, and the decision tree algorithm was used to analyze the structural and predictive relationships between the criteria. Correlation analysis was also performed to examine the relationships between the criteria. The findings suggest that the criteria of ‘Developing a foreign language’, ‘Developing a network’, ‘Graduating with a high average’, and ‘Developing program/software skills’ significantly impact students’ five-year career planning. The results reveal that the dimensions of academic guidance, industry collaboration, and skills development form a complementary structure. This study makes a theoretical contribution to the literature on applied education by presenting a multi-criterion, data-driven analysis model that integrates student perspectives with expert evaluations to develop feasible suggestions for strengthening the alignment between higher education and industry.

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Bu Makaleye Atıf Yap

Çiftçi, S., Özer, S. (2026). Evaluating an education model via Pythagorean fuzzy AHP and decision tree. International Journal of Assessment Tools in Education, Volume: 13 Issue: 3. https://doi.org/10.21449/ijate.1767701

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Kaynakça (36)

  1. Ağçay, F. (2024). Üçüncü nesil üniversiteler [Third generation universties]. Social Sciences Studies Journal, 5(49), 6314-6322. https://doi.org/10.26449/sssj.1898
  2. Bayrak, A. (2021). Sektörel bakış: Üniversite-Sanayi işbirliği üzerine bir değerlendirme [An evaluation on university industry cooperation]. Erciyes Academy, 35(2), 386 396. https://izlik.org/JA28ZM84NM
  3. Cengiz, E. (2014). Üniversite–Sanayi işbirliği üzerine değerlendirmeler [Assessments on university-industry collaboration]. Journal of Higher Education, 4(1), 1-8. https://izlik.org/JA63TK49HL
  4. Chen, S., & Lin, X. (2023). Application of decision tree algorithm in educational data mining. Curriculum and Teaching Methodology, 6(8), 120 127. https://doi.org/10.23977/curtm.2023.060818
  5. Çağıl, G., & Toklu, M.C. (2018). Sakarya Üniversitesi Endüstri Mühendisliği bölümünde uygulamalı mühendislik deneyimi modeli ve öğrenciler üzerindeki etkileri [Applied engineering experience model in Sakarya University Industrial Engineering department and effects on students]. Sakarya University Journal of Science, 22(2), 572-580. https://doi.org/10.16984/saufenbilder.336176
  6. Çalış, Ş., Turan, Ş.A., Maç, S.D., & Tan, E. (2020). İşyeri uygulamasının istihdam edilebilirliğe katkısı: Sakarya Üniversitesi İKY bölümü öğrencileri üzerine bir araştırma [Contribution of work-based learning to employability: A research on the students of HRM department of Sakarya University]. Sakarya Economics Journal, 9(4), 435-460. https://izlik.org/JA24UL53UZ
  7. Çiftçi, S., Pınarbaşı, M., Yazıcı, E., & Alakaş, H.M. (2023). Evaluation of the companies for applied education under fuzzy environment: A case in an industrial university. Neural Computing and Applications, 35(29), 21675-21695. https://doi.org/10.1007/s00521-023-08926-x
  8. Çiftçi, S., Pınarbaşı, M., & Alakaş, H.M. (2025). Evaluation of companies in applied education: Determining the criteria and a case study at an industrial university. International Journal of Information Technology & Decision Making, 24(7), 2079 2115. https://doi.org/10.1142/S0219622025500300
  9. Dang, Q., Luu, N., Pham, H., & Le, P. (2026). University–industry knowledge collaborations and students: A systematic literature review and research agenda. SSRN. https://doi.org/10.2139/ssrn.6151083
  10. Dikmen, M., & Baysal, H. (2017). Üniversite sanayi işbirliği kapsamında teknoloji fakültesi işyeri eğitimi modeli üzerine bir araştırma [Research on faculty of technology of workplace training model in the scope of university-industry cooperation]. International Journal of Sustainable Engineering and Technology, 1(1), 35-46. https://izlik.org/JA45AX34CE
  11. Gündüz, C., Karaçınar, H., Çiğdem, R., & Orak, Z. D. (2025). [Mesleki eğitimde kalite odaklı dönüşüm: Müfredat reformları, dijitalleşme ve sektörel işbirlikleri] Quality-oriented transformation in vocational education: Curriculum reforms, digitalization, and industry partnerships. Journal of Anadolu University Vocational Education and Practice 4(2), 106 130. https://doi.org/10.70756/anameud.1649717
  12. Hamoud, A., Hashim, A.S., & Awadh, W.A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5, 26-31. https://doi.org/10.9781/ijimai.2018.02.004
  13. Ho, C.S.M., Cai, Y., & Lee, J.C.K. (2026). Role models in policy learning: Establishing universities of applied sciences in Hong Kong. Studies in Higher Education, 1 22. https://doi.org/10.1080/03075079.2026.2617127
  14. Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP and fuzzy inference system. Safety Science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025
  15. Kaysi, F., & Gürol, A. (2017). Meslek yüksekokulu öğrencilerinin işyeri uygulamalı eğitim süreçlerinin değerlendirilmesi [Assessment of workplace practical training processes of vocational school students]. Kesit Academy Journal, 8, 266-280. https://izlik.org/JA78KS77BM
  16. Kaynak, S., & Yılmaz, M.K. (2022). Akademi ve iş dünyası perspektifinden üniversite-sanayi iş birliği engelleri [Challenges of university and industry cooperation from the perspective of academy and business world]. Ağrı İbrahim Çeçen Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 8(1), 239-270. https://doi.org/10.31463/aicusbed.1087445
  17. Kershaw, T.C., Bhowmick, S., Seepersad, C.C., & Hölttä-Otto, K. (2019). A decision tree-based methodology for evaluating creativity in engineering design. Frontiers in Psychology, 10, Article 32. https://doi.org/10.3389/fpsyg.2019.00032
  18. Kurt, Ü., & Yavuz, M. (2013). Üniversite-Sanayi işbirliği: Dünü, bugünü, geleceği [University-industry cooperation: Past, present, future]. Süleyman Demirel University Journal of the Institute of Science and Technology, 17(1), 50-57. https://izlik.org/JA74MF98FZ
  19. Maceika, A., Bugajev, A., Šostak, O.R., & Vilutienė, T. (2021). Decision tree and AHP methods application for projects assessment: A case study. Sustainability, 13(10), Article 5502. https://doi.org/10.3390/su13105502
  20. Mustajab, D., & Irawan, A. (2023). The effectiveness of vocational training programs on employment outcomes. Advances in Community Services Research, 1(2), 54 63. https://doi.org/10.60079/acsr.v1i2.344
  21. Ommering, B.W.C., & Munneke, E.L. (2025). Balancing education, research and practice in Dutch universities of applied sciences: A conceptual and contextual exploration. Discover Education, 4(1), Article 524. https://doi.org/10.1007/s44217-025-00973-x
  22. Peksatici, O., & Ergun, H.S. (2019). The barriers against effective university–industry collaboration: A study in Turkish aviation industry. Journal of Management, Marketing and Logistics, 6(1), 35-43. https://doi.org/10.17261/Pressacademia.2019.1032
  23. Qi, Y., & Feng, W. (2025). The effectiveness evaluation of industry-education integration model for applied universities under back propagation neural network. Scientific Reports, 15(1), Article 5597. https://doi.org/10.1038/s41598-025-90030-2
  24. Salimi, N., & Rezaei, J. (2015). Multi-criteria university selection: Formulation and implementation using a fuzzy AHP. Journal of Systems Science and Systems Engineering, 24(3), 293 315. https://doi.org/10.1007/s11518-015-5271-3
  25. Seppo, M., & Lilles, A. (2012). Indicators measuring university–industry cooperation. Discussions on Estonian Economic Policy, 20(1), 204-225. https://ssrn.com/abstract=2194394
  26. Taratukhin, V., Kupriyanov, Y.V., & Becker, J. (2016, June 26-29). Towards a framework for educational university–industry cooperation: Industry perspective [Paper presentation]. 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, United States. https://doi.org/10.18260/p.27059
  27. Tong, L., Zhang, J., Yue, L., Chen, L., & Wang, M. (2025). A neural network-based model for cultivating applied talents in the context of industry–education integration. Discover Artificial Intelligence, 5(1), Article 126. https://doi.org/10.1007/s44163-025-00302-z
  28. Tosunoğlu, E., Yılmaz, R., Özeren, E., & Sağlam, Z. (2021). Eğitimde makine öğrenmesi: Araştırmalardaki güncel eğilimler üzerine inceleme [Machine learning in education: A study on current trends in researches]. Journal of Ahmet Kelesoglu Education Faculty, 3(2), 178 199. https://izlik.org/JA95AS84SX
  29. Valiente Bermejo, M., Eynian, M., Malmsköld, L., & Scotti, A. (2022). University–industry collaboration in curriculum design and delivery: A model and its application in manufacturing engineering courses. Industry and Higher Education, 36(5), 615 622. https://doi.org/10.1177/09504222211064204
  30. Wei, J., Chan, S.H.J., & Autin, K. (2022). Assessing perceived future decent work securement among Chinese impoverished college students. Journal of Career Assessment, 30(1), 3 22. https://doi.org/10.1177/10690727211005653
  31. Wu, P. (2021). Analysis on the innovative application of industry-university-research collaborative education mode. In Proceedings of the 6th International Conference on Arts, Design and Contemporary Education (ICADCE 2020) (pp. 285 289). Atlantis Press. https://doi.org/10.2991/assehr.k.210106.055
  32. Yager, R.R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965. https://doi.org/10.1109/TFUZZ.2013.2278989
  33. Yalcinkaya, B., & Saydam, V. (2018). The effect of internship processes on students' professional expectations: A study on Marmara University department of information and document management. The Archive World, 20, 1-20. https://izlik.org/JA39XT28LF
  34. Zhang, Y., & Chen, X. (2023). Empirical analysis of university–industry collaboration in postgraduate education: A case study of Chinese universities of applied sciences. Sustainability, 15(7), Article 6252. https://doi.org/10.3390/su15076252
  35. Zhang, Y., Zhang, Z., & Chen, X. (2025). How to improve university–enterprise cooperation: Application of intuitionistic fuzzy multi-attribute decision-making method based on decision-maker preferences in university–enterprise cooperation satisfaction evaluation. Frontiers in Education, 10, Article 1614174. https://doi.org/10.3389/feduc.2025.1614174
  36. Zilyas, D., & Yılmaz, A. (2023). Makine öğrenmesi yöntemleri ile eğitim başarısının tahmini modeli [Prediction model of educational success with machine learning method]. Dicle University Journal of Engineering, 14(3), 437-447. https://doi.org/10.24012/dumf.1322273