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Journals / Osmangazi Journal of Educational Research / 2026 / Volume: 11 Issue: 1

Examining Occupation Fields of Programs According to Artificial Intelligence: Anadolu University Open Education System Case

Sefa Emre Öncü(ANADOLU UNIVERSITY)0009-0008-2314-4628
İrfan Süral(OSMANGAZI UNIVERSITY)0000-0003-2232-9483
Pages
—
Publication Date
2024-09-14
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
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AI Summary & Key Takeaways

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

The study underscores the potential for higher education institutes to share employment and labor force data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Anadolu University's Open Education System (OES) accommodates over one million students and has incorporated an AI-based Virtual Assistant for non-academic support since 2022.
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Key Findings & Data:Recognizing the need for comprehensive assistance, this study sought to leverage AI capabilities to offer job recommendations by extracting information from the objectives of 63 OES programs.
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Conclusion & Impact:The study underscores the potential for higher education institutes to share employment and labor force data.
🔬Design:Research Article
📍Scope:2022 Verileri
⚙️Variables:Higher Education · Open Education · Student Support

Abstract

Anadolu University's Open Education System (OES) accommodates over one million students and has incorporated an AI-based Virtual Assistant for non-academic support since 2022. While OES offers abundant information about its programs on its website, there is a notable absence of support services providing job recommendations related to students' chosen programs. This gap in student support extends to the post-graduation phase, with the Virtual Assistant lacking a concept for guiding students in finding employment opportunities. Recognizing the need for comprehensive assistance, this study sought to leverage AI capabilities to offer job recommendations by extracting information from the objectives of 63 OES programs. The initial inquiry involved requesting AI-generated job recommendations based on the stated objectives of these programs. Subsequently, the Virtual Assistant was tasked with providing insights into the occupation fields associated with OES programs. Analysis of the AI's responses, along with the classification of occupations according to the International Standards of Classifications of Occupations (ISCO) and the International Standard Classification of Education (ISCED), forms the core of this study. Contrary to trends observed in most European countries, the predominant number of graduates in Türkiye emerges from business and management fields. However, the correlation between graduation rates and subsequent job placements appears suboptimal within the labor force and employment landscape. The study advocates for the integration of AI in offering job recommendations, incorporating graduation and employment rates. This approach enables students to seek guidance on suitable programs aligned with their skills, fostering a more informed decision-making process. The study underscores the potential for higher education institutes to share employment and labor force data.

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Cite This Article

Öncü, S. E., Süral, İ. (2024). Examining Occupation Fields of Programs According to Artificial Intelligence: Anadolu University Open Education System Case. Osmangazi Journal of Educational Research, Volume: 11 Issue: 1. https://doi.org/10.59409/ojer.1407007

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References (49)

  1. Akoğlan Kozak, M., & Dalkıranoğlu, T. (2013). Mezun öğrencilerin kariyer algılamaları: Anadolu üniversitesi örneği. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13(1), 41-52.
  2. Armstrong, C. (2021). Key methods used in qualitative document analysis. SSRN. https://ssrn.com/abstract=3996213
  3. Aydemir, L. (2018). Üniversite öğrencilerinin meslek tercihlerini belirleyen faktörlere yönelik bir inceleme. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6(5), 713–723. https://doi.org/10.18506/ANEMON.378084
  4. Babori, A., Zaid, A., & Fihri Fassi, H. (2019). Research on MOOCs in major referred journals. The International Review of Research in Open and Distributed Learning, 20(3). https://doi.org/10.19173/IRRODL.V20I4.4385
  5. Baykal, M. (2017). Türkiye yeterlilikler çerçevesi(TYÇ)’nin öğrenci değerlendirme programı (PISA) açısından değerlendirilmesi. Yeditepe Üniversitesi Eğitim Fakültesi Dergisi, 6(8), 69–79. https://dergipark.org.tr/en/pub/edu7/issue/36504/414111
  6. Bozkurt, A. (2013). Mega üniversitelerde öğrenci destek hizmetleri. Akademik Bilişim 2013 –XV. Akademik Bilişim Konferansı Bildirileri.
  7. Can, E. (2019). Açık ve uzaktan yükseköğretim mezunları üzerine bir değerlendirme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 5(3), 81–105. https://dergipark.org.tr/en/pub/auad/issue/50201/645988
  8. Chhinzer, N., & Russo, A. M. (2018). An exploration of employer perceptions of graduate student employability. Education + Training, 60(1), 104–120. https://doi.org/10.1108/ET-06-2016-0111
  9. Creswell, J. W. (2013). Qualitative ınquiry and research design. SAGE.
  10. Dunn, A. L., Briers, G. E., Moore, L. L., Odom, S. F., & Bailey, K. J. (2021). The places and spaces student affairs practitioners should learn and practice leadership educator competencies. Journal of Leadership Education, 66–85. https://doi.org/10.12806/V20/I4/R6
  11. Fitria, T. N. (2023). Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay. ELT Forum: Journal of English Language Teaching, 12(1), 44–58. https://doi.org/10.15294/ELT.V12I1.64069
  12. Ganzeboom, H. B. G., & Treiman, Donald. J. (1996). Internationally comparable measures of occupational status for the 1988 ınternational standard classification of occupations. Socıal Scıence Research, 25(10), 201–239.
  13. Gönültaş Çelik, M. (2019). İşgücü piyasasının bir sorun alanı olarak uyumsuz eşleşme. Yönetim ve Çalışma Dergisi, 3(2), 223–241. https://web.s.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=0&sid=daf7d37e-e10c-46be-a99b-1687107f1d62%40redis
  14. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925/FORMAT/EPUB
  15. Harvey, L. (2001). Defining and measuring employability. Quality in Higher Education, 7(2), 97–109. https://doi.org/10.1080/13538320120059990
  16. Holmegaard, H. T., Ulriksen, L. M., & Madsen, L. M. (2014). The process of choosing what to study: a longitudinal study of upper secondary students’ ıdentity work when choosing higher education, 58(1), 21–40. https://doi.org/10.1080/00313831.2012.696212
  17. Hou, I., Metille, S., Li, Z., Man, O., Zastudil, C., & MacNeil, S. (2024). The effects of generative AI on computing students’ help-seeking preferences. https://doi.org/https://doi.org/10.1145/3636243.3636248 Ilieva-Trichkova, P., & Boyadjieva, P. (2017). Labour and social transformation in central and eastern europe (V. Delteil & V. Kirov, Eds.).
  18. Karadağ, E. (2010). Eğitim bilimleri doktora tezlerinde kullanılan araştırma modelleri: nitelik düzeyleri ve analitik hata tipleri 1. Educational Administration: Theory and Practice, 16(1), 49–71.
  19. Karadağ, E., & Yücel, C. (2017). Türkiye üniversite memnuniyet araştırması [TÜMA-2017]: Rapor Özeti. Yükseköğretim Dergisi, 7(2), 132–144. https://doi.org/10.2399/yod.17.016
  20. Kaya, H. E. (2016). Yaşam boyu yetişkin eğitimi. Nobel Akademik Yayıncılık.
  21. Kehm, B. M. (2010). Quality in european higher education: the ınfluence of the bologna process, change: The magazine of higher learning. 42(3), 40–46. https://doi.org/10.1080/00091381003704677
  22. Kim, J. H., Kim, M., Kwak, D. W., & Lee, S. (2022). Home-tutoring services assisted with technology: Investigating the role of artificial ıntelligence using a randomized field experiment. Journal of Marketing Research, 59(1), 79–96. https://doi.org/10.1177/00222437211050351/ASSET/IMAGES/LARGE/10.1177_00222437211050351-FIG6.JPEG
  23. Kingchang, T., Chatwattana, P., & Wannapiroon, P. (2024). Artificial ıntelligence chatbot platform: AI chatbot platform for educational recommendations in higher education. International Journal of Information and Education Technology, 14(1). https://doi.org/10.18178/ijiet.2024.14.1.2021
  24. Kline, R. R. (2011). Cybernetics, automata studies, and the dartmouth conference on artificial ıntelligence. IEEE Annals of the History of Computing, 33(4), 5–16. https://doi.org/10.1109/MAHC.2010.44
  25. Korkut-Owen, F., Kepir, D. D., Özdemir, S., Ulaş, Ö., & Yilmaz, O. (2013). Üniversite öğrencilerinin bölüm seçme nedenleri. Mersin University Journal of the Faculty of Education, 8(3), 135–151. https://doi.org/10.17860/EFD.87701
  26. Kromhout, H. (2003). Options for analysis labour statistics The use of occupation and industry classifications in general population studies. International Journal of Epidemiology, 32, 419–428. https://doi.org/10.1093/ije/dyg080
  27. Liu, J., Liu, C., Zhou, P., Kong, H., Lv, R., Zhou, K., & Zhang, Y. (2023). Is ChatGPT a good recommender? A preliminary study. The 1st Workshop on Recommendation with Generative Models, October 21â•fi25, 2023, Birmingham, United Kingdom, 1. https://doi.org/10.1145/nnnnnnn.nnnnnnn
  28. Melina, M., Putra, E. K., Hadiana, A. I., & Kusumaningtyas, V. A. (2023). Web-based information system the offices bureau of student affairs with database modeling and design. Sıxth Internatıonal Conference Of Mathematıcal Scıences (ICMS 2022), 2879(1), 030002. https://doi.org/10.1063/5.0129003/2915880
  29. Negnevitsky, M. (2005). Artificial ıntellige (2nd ed.). Pearson Education.
  30. Ng, B., Lee, A. N., & Chua, B. L. (2022). Preparing graduates for future workplace: development and validation of work readiness assessment ınventory (WRAI). 13–35. https://doi.org/10.1007/978-3-031-05716-8_2
  31. Ortiz, L. (2008). Evaluation of the ISCED-97 for the Spanish system of education. The International Standard Classification of Education (ISCED-97, 15, 125–248.
  32. Öztürk, Ö. (2018). Açıköğretim programlarına kayıt yaptıran ve yaptırmayan öğrencilerin açıköğretim sistemine yönelik görüşleri. AUAd, 157–170. https://dergipark.org.tr/en/download/article-file/465604
  33. Rahmat, M., Ahmad, K., Idris, S., & Zainal, N. F. A. (2012). Relationship between Employability and Graduates’ Skill. Procedia - Social and Behavioral Sciences, 59, 591–597. https://doi.org/10.1016/J.SBSPRO.2012.09.318
  34. Silvestru, C.-I., Ion, V., Botez (Constantin), C., & Icociu, V.-C. (2018). ISCED Classification Influence on E-Learning Education Systems. Informatica Economica, 22(4), 80–88. https://doi.org/10.12948/issn14531305/22.4.2018.07
  35. Simpson, O. (2012). Supporting Students for Success in Online and Distance Education (3rd ed.). Routledge.
  36. Stiwne, E. E., & Alves, M. G. (2010). Higher education and employability of graduates: Will Bologna make a difference? European Educational Research Journal, 9(1), 32–44. https://doi.org/10.2304/EERJ.2010.9.1.32
  37. Süral, İ. (2008). Yeni teknolojiler ışığında uzaktan eğitimde açıklık, uzaktanlık ve öğrenme. XIII. Türkiye’de Internet Konferansı Bildirileri, 22-23 Aralık, Orta Doğu Teknik Üniversitesi, Ankara.
  38. Tait, A. (2010). Planning student support for open and distance learning. Open Learning: The Journal of Open, Distance and e-Learning, 5(3), 287–299. https://doi.org/10.1080/713688410
  39. Tarvid, A. (2015). The role of ındustry in the prevalence of overeducation in Europe. Procedia Economics and Finance, 30, 876–884. https://doi.org/10.1016/S2212-5671(15)01337-4
  40. Thorpe, M. (2002). Rethinking learner support: the challenge of collaborative online learning. Open Learning, 17(2), 105–119. https://doi.org/10.1080/02680510220146887A
  41. Verma, A., Lamsal, K., & Verma, P. (2022). An investigation of skill requirements in artificial intelligence and machine learning job advertisements. Industry and Higher Education, 36(1), 63–73. https://doi.org/10.1177/0950422221990990/ASSET/IMAGES/LARGE/10.1177_0950422221990990-FIG3.JPEG
  42. Wan, W., Ge, C. B., Friesen, M. C., Locke, S. J., Russ, D. E., Burstyn, I., Baker, C. J. O., Adisesh, A., Lan, Q., Rothman, N., Huss, A., Van Tongeren, M., Vermeulen, R., & Peters, S. (2023). Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison. Annals of Work Exposures and Health, 67(5), 663–672. https://doi.org/10.1093/ANNWEH/WXAD002
  43. Wegmann, R. G. (1979). Job search assistance: A Review.
  44. Weligamage, S., & Samudrika Weligamage, S. (2014). Graduates’ Employability Skills: Evidence from Literature Review Graduates" Employability Skills: Evidence from Literature Review. 115. https://www.researchgate.net/publication/266014502
  45. Xiong, S., & Lee, S. E. (2011). Hmong Students in Higher Education and Academic Support Programs by Soua Xiong and Song E Hmong Students in Higher Education and Academic Support Programs. Lee, Hmong Studies Journal, 12, 1–20.
  46. Yin, R. K. (2009). Case Study Research: Design and Methods (4th ed.). SAGE.
  47. Zgaga, P. (2006). Looking out: The Bologna Process in a Global Setting. Norwegian Ministry of Education and Research.
  48. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021. https://doi.org/10.1155/2021/8812542
  49. Züll, C. (2016). The Coding of Occupations. https://doi.org/10.15465/gesis-sg_en_019