Journals / Scientific Journal of Finance and Financial Law Studies / 2026 / Cilt: 6 Sayı: 1

Measuring The Perception Levels of Students Take Accounting Courses in Universities Located in Van Lake Basin about Data Mining

Van Gölü Havzasında Yer Alan Üniversitelerde Muhasebe Dersi Alan Öğrencilerin Veri Madenciliği Konusundaki Algı Düzeylerinin Ölçülmesi

Nazan Güngör Karyağdı(BİTLİS EREN ÜNİVERSİTESİ)0000-0003-3938-4147
Yakup Aslan(MUŞ ALPARSLAN ÜNİVERSİTESİ)0000-0001-9833-8840
Pages
31–47
Publication Date
2026-03-27
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AI Summary & Key Takeaways

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

In addition, although the study indicates that the perception of data mining increases as the level of education of the students increases, it is well below acceptable limits.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the advancement of technology, it is very important to transform the data obtained to be used in business processes in both public institutions/organizations and the private sector into relevant and usable information for decision-making.
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Key Findings & Data:For this reason, many modeling tools have been developed and tried to be integrated into the process.
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Conclusion & Impact:In addition, although the study indicates that the perception of data mining increases as the level of education of the students increases, it is well below acceptable limits.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Muhasebe · Muhasebe Eğitimi · Veri Madenciliği

Abstract

With the advancement of technology, it is very important to transform the data obtained to be used in business processes in both public institutions/organizations and the private sector into relevant and usable information for decision-making. For this reason, many modeling tools have been developed and tried to be integrated into the process. Data mining, one of these models, provides businesses with the opportunity to make strategic decisions by obtaining data, storing data, and feeding information at the decision stage. Data mining, which is used effectively in all areas, contributes in many aspects such as generating information in the accounting information system, transforming it into useful information, reporting and analyzing it. Data mining, which is increasingly used in accounting education, which includes the accounting information system, is of great benefit to both academicians who provide accounting education and students who receive accounting education. In the study, it was aimed to measure the perception levels of students studying at universities in Van Gölü Basin on data mining. The data obtained were analyzed in the SPSS package program and as a result of the study, it was seen that the perception levels of the students were not at a sufficient level. In addition, although the study indicates that the perception of data mining increases as the level of education of the students increases, it is well below acceptable limits.

Özet

Teknolojinin ilerlemesiyle hem kamu kurum/kuruluşlarında hem de özel sektörde iş süreçlerinde faydalanılmak üzere elde edilen verilerin karar alma aşamasında anlamlı ve kullanılabilir bilgiye dönüştürülmesi oldukça önemlidir. Bu sebeple birçok modelleme aracı geliştirilmiş ve sürece entegre edilmeye çalışılmıştır. Bu modellerden biri olan veri madenciliği, verilerin elde edilmesi, verilerin saklanması, karar aşamasında bilgi beslemesi yaparak işletmelere stratejik kararlar alma olanağı sunmaktadır. Bütün alanlarda etkin bir şekilde kullanılan veri madenciliği muhasebe bilgi sisteminde de bilgilerin üretilerek faydalı bilgiye dönüştürülmesi, raporlanması ve analiz etmesi gibi birçok açıdan katkı sunmaktadır. Muhasebe bilgi sistemini içine alan muhasebe eğitiminde de giderek kullanımı yaygınlaşan veri madenciliği, hem muhasebe eğitimi veren akademisyenler hem de muhasebe eğitimi alan öğrenciler için büyük fayda sağlamaktadır. Çalışmada Van Gölü Havzası’nda yer alan üniversitelerde eğitim gören öğrencilerin veri madenciliği konusundaki algı düzeylerinin ölçülmesi amaçlanmıştır. Elde edilen veriler SPSS paket programında analiz edilmiş ve çalışma sonucunda öğrencilerin algı düzeylerinin yeterli düzeyde olmadığı görülmüştür. Ayrıca çalışma, öğrencilerin eğitim düzeyi arttıkça veri madenciliği hususunda algının arttığına işaret etse de kabul edilebilir sınırların oldukça altında olduğunu göstermektedir

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Cite This Article

Karyağdı, N. G., Aslan, Y. (2026). Measuring The Perception Levels of Students Take Accounting Courses in Universities Located in Van Lake Basin about Data Mining. Scientific Journal of Finance and Financial Law Studies, Cilt: 6 Sayı: 1, 31–47. https://doi.org/10.71284/sjffls.2026613

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References (50)

  1. Abu Al-Khair, O.A.M. (2019), The role of using data mining methods to improve the auditor’s estimates of the existence of material mistakes in the financial statements: A field study in the egyptian business environment, Contemporary Business Studies Journal, 5(7), 18-27.
  2. Alagöz, A., Öge, S.& Ortakarpuz, M. (2014). Bir kurumsal zekâ teknolojisi olarak veri madenciliği ile muhasebe bilgi sistemi ilişkisi, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Dr. Mehmet Yıldız Özel Sayısı, 1-21
  3. Al Chahadah, A., Refae, G.& Qasim, A.(2018). The use of data mining techniques in accounting and finance as a corporate strategic tool: an empirical investigation on banks operating in emerging economies, International Journal of Economics and Business Research, 15(4), 442-452. https://doi.org/10.1504/IJEBR.2018.092149
  4. Al-Bakri, M.A.A. (2016). A proposed model for developing the review process using the data mining method, Scientific Journal of the Faculties of Commerce Sector, 16(2), 12-26
  5. Al Dafai, A. (2018). The role of using data mining method in assigning the opinion of the external audeauditorut the discovery of errors in the financial reports and their impact on the audit process, Master, s thesis business and corporate Administration, Aydın University, Turkey.
  6. Amani, F.A.& Fadlalla, A.M. (2017). Data mining applications in accounting: A review of the literature and organizing framework, International Journal of Accounting Information Systems, 24, 32-58. https://doi.org/10.1016/j.accinf.2016.12.004
  7. Belfo, F., & Trigo, A. (2013). Accounting information systems: Tradition and future directions. Procedia Technology, 9, 536-546.
  8. Binici, F. Ö. (2025). Veri madenciliği ve muhasebede kullanımı: Muhasebe dolandırıcılığının tespitinde en çok kullanılan yöntemler. Avrasya Sosyal Ve Ekonomi Araştırmaları Dergisi, 12(4), 318-345.
  9. Chamizo-Gonzalez, J., Cano-Montero, E.I., Urquia-Grande, E.& Muñoz-Colomina, C.I.(2015). Educational data mining for improving learning outcomes in teaching accounting within higher education, International Journal of Information and Learning Technology, 32(5),272-285. DOI 10.1108/IJILT-08-2015-0020
  10. Chitra, K., & Subashini, B. (2013). Data mining techniques and its applications in banking sector. International Journal of Emerging Technology and Advanced Engineering, 3(8), 219–226. https://tarjomefa.com/wp-content/uploads/2018/05/9087-English-TarjomeFa.pdf
  11. Chung, H. M. & Grey, P. (1999), Special section: Data mining, Journal of Management Information Systems, 16(1), 1-16.
  12. Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI Magazine, 13(3), 57-70,. https://doi.org/10.1609/aimag.v13i3.1011
  13. Gandy, O. (2019). Data mining, surveillance, and discrimination in the post-9/11 environment. In K. Haggerty & R. Ericson (Eds.), The new politics of surveillance and visibility (pp. 363–384). University of Toronto Press. https://doi.org/10.3138/9781442681880-016
  14. Grob, H.L., Bensberg, F. & Dewanto, B.L. (2004). Developing, deploying, using and evaluating an open source learning management system, Journal of Computing and Information Technology, 12(2), 127-134, http://hrcak.srce.hr/cit_ojs/index.php/ CIT/article/viewFile/1537/1241
  15. Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). New York, NY: Morgan Kaufmann.
  16. Handoko, B.L., Reinaldy, N., Wifasari, S., Prasetyo, H.& Meinarsih, T.(2023). Impact of data mining, big data analytics and data visualization on audit quality, ICCMB '23: Proceedings of the 2023 6th International Conference on Computers in Management and Business Pp: 100 – 105, https://doi.org/10.1145/3584816.358483
  17. Hayran, M. (2011). Sağlık araştırmaları için temel istatistik (1. Basım). Art Ofset Matbaacılık Yayıncılık Organizasyon, Ankara
  18. Heiner, C., Beck, J. & Mostow, J. (2004). Lessons on using its data to answer educational research questions., Proceedings of the ITS2004 Workshop on Analyzing Student–Tutor Interaction Logs to Improve Educational Outcomes, 1-9.
  19. Heishan, S. J. F., & Saqour, M. K. (2022). The impact of applying the data mining method in achieving the quality of financial reports from the point of view of the Iraqi external auditors. Tikrit Journal of Administrative and Economic Sciences, 18(59, 2), 84–99. https://doi.org/10.25130/tjaes.18.59.2.6
  20. Hussain, A.M., Al-Kooheji, E.K.& Wadi, R.A. (2023). Data mining in accounting and banking: Applications, opportunities and challenges. In: El Khoury, R., Nasrallah, N. (eds) Emerging Trends and Innovation in Business and Finance. Contributions to Management Science. Springer, Singapore. https://doi.org/10.1007/978-981-99-6101-6_62
  21. Jackson, J., (2002), Data mining: A conceptual overview, Communications of the Association for Information Systems, 8,.267-296.
  22. Kirkos, E., Spathis, C. & Manolopoulos, Y.(2007). Data mining techniques for the detection of fraudulent financial statements, Expert Systems with Applications, 32(4), 995-1003.
  23. Leskovec, J., Rajaraman, A., & Ullman, J. D. (2020). Mining of massive data sets (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781108684163
  24. Lu, Y.(2019). Financial accounting intelligence management of internet of things enterprises based on data mining algorithm, Journal of Intelligent& Fuzzy Systems,37(5), 5915-5923.
  25. Mazza, R. & Milani, C. (2005), Exploring usage analysis in learning systems: gaining insights from visualizations”, workshop on Usage Analysis in Learning Systems at 12th International Conference on Artificial Intelligence in Education, Amsterdam, 18, July, pp: 65-72.
  26. Mohaisen, L.H.A., Al-Abedi, L.T.K. & Jothr, L.O.A. (2023). Data mining and its effects on the accounting and auditing profession: A literature review, The Journal Of Administration & Economics, 48(137), 230-239. https://doi.org/10.31272/jae.i137.1151
  27. Mostow, J. (2004). Some useful design tactics for mining its data. Proceedings of the ITS2004 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, Maceio, 1-8.
  28. Nilakant, K. &Mitrovic, A. (2005), Application of data mining in constraint-based intelligent tutoring systems, Proceedings of the Artificial Intelligence in Education, AIED,896-898.
  29. Papík, M.& Papíkova, L. (2021). Application of selected data mining techniques in unintentional accounting error detection, Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(1),185-201.
  30. Papík, M.& Papíkova, L. (2022). Detecting accounting fraud in companies reporting under US GAAP through data mining, International Journal of Accounting Information Systems, 45, 2-19. https://doi.org/10.1016/j.accinf.2022.100559
  31. Pena-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications,41(4), 1432-1462.
  32. Qatawneh, A. (2022). The influence of data mining on accounting information system performance: A mediating role of information technology infrastructure. Journal of Governance & Regulation, 11(1), 141–151. https://doi.org/10.22495/jgrv11i1art13
  33. Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R.S.J.D. (Eds.). (2010). Handbook of Educational Data Mining (1st ed.). CRC Press. https://doi.org/10.1201/b10274
  34. Savaş, S., Toplaoğlu, N.& Yılmaz, M.(2012). Veri madenciliği ve Türkiye’deki uygulama örnekleri, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi,11(21), 1-23
  35. Seyitoğulları, O., (2025). Adli muhasebecilik farkındalık düzeyinin belirlenmesi: Batman ilinde smmm ve avukatlar üzerine bir araştırma. Abant Sosyal Bilimler Dergisi, 25(3), 1482-1497. doi: 10.11616/asbi.1742549
  36. Sithole, S. T. M., Ran, G., de Lange, P., Tharapos, M., O’Connell, B., & Beatson, N. (2022). Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs? Accounting Education, 32(4), 409–444. https://doi.org/10.1080/09639284.2022.2075707
  37. Tan, P., Steinbach, M., & Kumar, V. (2011). Introduction to data mining (2nd ed.). New York, NY: Pearson.
  38. Tekin, A.& Öztekin, Z.(2018). Eğitsel veri madenciliği ile ilgili 2006-2016 yılları arasında yapılan çalışmaların incelenmesi, Eğitim Teknolojisi Kuram ve Uygulama, 8(2), 68-88.
  39. Thuraisingham, B. (1998). Data Mining: Technologies, Techniques, Tools, and Trends (1st ed.). CRC Press. https://doi.org/10.1201/b16553
  40. Wang, Y.& Wang, Z.(2016). Integrating data mining into managerial accounting system: Challenges and opportunities, Chinese Business Review, 15(1), 33-41. DOI: 10.17265/1537-1506/2016.01.004
  41. Wang, Y. (2024). Security analysis of accounting computerized information system based on data mining and neural networks, Third International Conference on Distributed Computing and Electrical Circuits and
  42. Electronics (ICDCECE), Ballari, India, 2024, pp. 1-5, DOİ: 10.1109/ICDCECE60827.2024.10548746.
  43. Winne, P. H., & Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. JEDM-Journal of Educational Data Mining, 5(1), 1-8.
  44. Wu, Q.-F. (2021). Distance teaching method of accounting informatization course based on big data mining. In W. Fu, S. Liu, & J. Dai (Eds.), Proceedings of International Conference on E-Learning, E-Education, and Online Training (pp. 155–166). Springer. https://doi.org/10.1007/978-3-030-84383-0_14
  45. Vizyonergenç (2024). Beş temel soruda veri madenciliği, https://vizyonergenc.com/icerik/5-temel-soruda-veri-madenciligi-data-mining-nedir? (Erişim Tarihi: 18.02.2025).
  46. Yada, K. (2002). The future direction of active mining in the business world. (H. Motoda Ed.), Active Mining: New Direction of Data Mining (3-9), Amsterdam: IOS Press.
  47. Zhang, X. (2021). Application of data mining and machine learning in management accounting information system, Journal of Applied Science and Engineering, 24(5), 813-820. https://doi.org/10.6180/jase.202110_24(5).0018
  48. Zhang, M. (2024). Reform and effectiveness assessment of accounting teaching in colleges and universities based on multi-source data mining, Applied Mathematics and Nonlinear Sciences, 9(1),1-19. https://doi.org/10.2478/amns-2024-1350
  49. Jackson, J., (2002), Data mining: A conceptual overview, Communications of the Association for Information Systems, 8,.267-296.
  50. Savaş, S., Toplaoğlu, N.& Yılmaz, M.(2012). Veri madenciliği ve Türkiye’deki uygulama örnekleri, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi,11(21), 1-23