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Readiness of Higher Education Institutions for Big Data

Yıl 2024, Cilt: 3 Sayı: 2, 119 - 140, 30.12.2024

Öz

The objective of this study is to evaluate the degree of readiness exhibited by higher education institutions in Turkey with regard to the integration of big data technologies in the context of expert recruitment. Big data signifies extensive and intricate datasets that surpass the capacity of conventional database systems, while big data analytics pertains to the process of deriving valuable insights from these data. In the present study, an assessment of the readiness of universities in Turkey for big data technologies was conducted across the domains of leadership, talent management, software technology, data-driven decision making and organizational culture. The data for the study were obtained through the participation of 164 IT specialists from 86 universities. The research findings indicate significant variations across three key domains: leadership and talent management in data scientists, technology in data engineers, and talent management, technology, and decision-making in business analysts. Additionally, the study identified software developers and web designers as having substantial influence on technology and decision-making dimensions. A noteworthy finding was the revelation that 94.3% of the participating universities do not employ experts in the big data engineer position. These findings underscore the necessity for universities to enhance and diversify the employment of experts in order to leverage big data technologies effectively. It can be concluded that the establishment of a balanced portfolio of experts in the field of big data analytics is imperative for the success of higher education institutions.

Kaynakça

  • Abtew, A., & Endebu, A. (2023). The role of big data analytics in improving teacher training in developing countries: A literature review. https://doi.org/10.21203/rs.3.rs-3111391/v1
  • Adam, N. R., Wieder, R., & Ghosh, D. (2017). Data science, learning, and applications to biomedical and health sciences. Annals of the New York Academy of Sciences, 1387(1), 5–11. https://doi.org/10.1111/nyas.13309
  • Adrian, C., Abdullah, R., Atan, R., & Jusoh, Y. Y. (2018). Expert review on big data analytics implementation model in data-driven decision-making. In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) (pp. 1–5).
  • Akrami, K., Akrami, M., Akrami, F., & Hakimi, M. (2024). Investigating the integration of big data technologies in higher education settings. Indonesian Journal of Multidisciplinary on Social and Technology, 2(2), 1–12. https://doi.org/10.31004/ijmst.v2i2.296
  • Aldholay, A., Isaac, O., Jalal, A. N., Anor, F. A., & Mutahar, A. M. (2021). Towards a better understanding of the organizational characteristics that affect acceptance of big data platforms for academic teaching. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 9(3), 766–773. https://doi.org/10.52549/ijeei.v9i3.2902
  • Alkhalil, A., Abdallah, M. A. E., Alogali, A., & Aljaloud, A. (2021). Applying big data analytics in higher education: A systematic mapping study. International Journal of Information and Communication Technology Education (IJICTE), 17(3), 29–51. https://doi.org/10.4018/IJICTE.20210701.oa3
  • Al-Sai, Z. A., Abdullah, R., & Husin, M. H. (2020). Critical success factors for big data: A systematic literature review. IEEE Access, 8, 118940–118956. https://doi.org/10.1109/ACCESS.2020.3005461
  • Alsheikh, N. (2019). Developing an integrated framework to utilize big data for higher education institutions in Saudi Arabia. International Journal of Computer Science and Information Technology, 11(1), 31–42. https://doi.org/10.5121/ijcsit.2019.11103
  • Altaye, A. A., & Nixon, S. Jr. (2019). A comparative study on big data applications in higher education. International Journal of Emerging Trends in Engineering Research, 7(12), 739–745. https://doi.org/10.30534/ijeter/2019/027122019
  • Altunışık, R. (2015). Büyük veri: Fırsatlar kaynağı mı yoksa yeni sorunlar yumağı mı? Yildiz Social Science Review, 1(1), 45–76.
  • Ang, K. L.-M., Ge, F. L., & Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8, 116392–116414. https://doi.org/10.1109/ACCESS.2020.2994561
  • Ariansyah, K., Setiawan, A. B., Hikmaturokhman, A., Ardison, A., & Walujo, D. (2024). Big data readiness in the public sector: An assessment model and insights from Indonesian local governments. Journal of Science and Technology Policy Management. Advance online publication. https://doi.org/10.1108/JSTPM-01-2023-0010
  • Attaran, M., Stark, J., & Stotler, D. (2018). Opportunities and challenges for big data analytics in US higher education: A conceptual model for implementation. Industry and Higher Education, 32(3), 169–182. https://doi.org/10.1177/0950422218770937
  • Baş, T. (2010). Anket. Ankara: Seçkin.
  • Berman, J. J. (2013). Principles of big data: Preparing, sharing, and analyzing complex information. Amsterdam: Elsevier, Morgan Kaufmann.
  • Brynjolfsson, E., & McAfee, A. (2013). Is your company ready for big data? Harvard Business Review. https://hbr.org/web/2013/06/assessment/is-your-company-ready-for-big-data adresinden 8 Mart 2022 tarihinde alınmıştır.
  • Casado, R., & Younas, M. (2015). Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience, 27(8), 2078–2091. https://doi.org/10.1002/cpe.3398
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  • Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer Integrated Manufacturing, 62, 1–20. https://doi.org/10.1016/j.rcim.2019.101861
  • Dabhade, K. R. (2014). Big data: An overview. International Journal of Scientific & Technology Research, 3(10), 255–257.
  • Davenport, T. (2018). Big data @ work. Harvard Business School Publishing Corporation.
  • Dhayne, H., Chamoun, R. K., & Sokhn, M. (2018). Survey: When semantics meet crowdsourcing to enhance big data variety. In 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM) (pp. 1–6).
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YÜKSEKÖĞRETİM KURUMLARININ BÜYÜK VERİYE HAZIRLIĞI

Yıl 2024, Cilt: 3 Sayı: 2, 119 - 140, 30.12.2024

Öz

Bu çalışmanın amacı, Türkiye’deki yükseköğretim kurumlarının büyük veri teknolojilerine hazırlık seviyelerini uzman istihdamı bağlamında değerlendirmektir. Büyük veri, geleneksel veri tabanı sistemlerinin kapasitesini aşan büyük ve karmaşık veri kümelerini ifade ederken, büyük veri analitiği bu verilerden anlamlı içgörüler elde etme sürecini tanımlamaktadır. Araştırmada, Türkiye’deki üniversitelerin büyük veri teknolojilerine hazırlık düzeyleri, liderlik, yetenek yönetimi, yazılım teknolojisi, veriye dayalı karar verme ve kurum kültürü boyutları üzerinden değerlendirilmiştir. Çalışmanın verileri, 86 üniversiteden 164 bilgi işlem uzmanının katılımıyla elde edilmiştir. Veri bilimcilerin liderlik ve yetenek yönetimi boyutlarında, veri mühendislerinin teknoloji boyutunda, iş analistlerinin yetenek yönetimi, teknoloji ve karar verme boyutlarında anlamlı farklılıklar gözlemlenmiştir. Ayrıca, yazılım geliştiricilerin ve web tasarımcılarının teknoloji ve karar verme boyutlarında önemli etkileri olduğu tespit edilmiştir. Dikkat çekici bir bulgu olarak, araştırmaya katılan üniversitelerin %94,3’ünde büyük veri mühendisi pozisyonunda uzman istihdam edilmediği ortaya çıkmıştır. Bu bulgular, üniversitelerin büyük veri teknolojilerini etkin bir şekilde kullanabilmeleri için uzman istihdamını artırmaları ve çeşitlendirmeleri gerekliliğini ortaya koymaktadır. Büyük veri analitiği alanında dengeli bir uzman portföyünün oluşturulması, yükseköğretim kurumlarının başarısı için kritik öneme sahip olduğu belirtilebilir.

Kaynakça

  • Abtew, A., & Endebu, A. (2023). The role of big data analytics in improving teacher training in developing countries: A literature review. https://doi.org/10.21203/rs.3.rs-3111391/v1
  • Adam, N. R., Wieder, R., & Ghosh, D. (2017). Data science, learning, and applications to biomedical and health sciences. Annals of the New York Academy of Sciences, 1387(1), 5–11. https://doi.org/10.1111/nyas.13309
  • Adrian, C., Abdullah, R., Atan, R., & Jusoh, Y. Y. (2018). Expert review on big data analytics implementation model in data-driven decision-making. In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP) (pp. 1–5).
  • Akrami, K., Akrami, M., Akrami, F., & Hakimi, M. (2024). Investigating the integration of big data technologies in higher education settings. Indonesian Journal of Multidisciplinary on Social and Technology, 2(2), 1–12. https://doi.org/10.31004/ijmst.v2i2.296
  • Aldholay, A., Isaac, O., Jalal, A. N., Anor, F. A., & Mutahar, A. M. (2021). Towards a better understanding of the organizational characteristics that affect acceptance of big data platforms for academic teaching. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 9(3), 766–773. https://doi.org/10.52549/ijeei.v9i3.2902
  • Alkhalil, A., Abdallah, M. A. E., Alogali, A., & Aljaloud, A. (2021). Applying big data analytics in higher education: A systematic mapping study. International Journal of Information and Communication Technology Education (IJICTE), 17(3), 29–51. https://doi.org/10.4018/IJICTE.20210701.oa3
  • Al-Sai, Z. A., Abdullah, R., & Husin, M. H. (2020). Critical success factors for big data: A systematic literature review. IEEE Access, 8, 118940–118956. https://doi.org/10.1109/ACCESS.2020.3005461
  • Alsheikh, N. (2019). Developing an integrated framework to utilize big data for higher education institutions in Saudi Arabia. International Journal of Computer Science and Information Technology, 11(1), 31–42. https://doi.org/10.5121/ijcsit.2019.11103
  • Altaye, A. A., & Nixon, S. Jr. (2019). A comparative study on big data applications in higher education. International Journal of Emerging Trends in Engineering Research, 7(12), 739–745. https://doi.org/10.30534/ijeter/2019/027122019
  • Altunışık, R. (2015). Büyük veri: Fırsatlar kaynağı mı yoksa yeni sorunlar yumağı mı? Yildiz Social Science Review, 1(1), 45–76.
  • Ang, K. L.-M., Ge, F. L., & Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8, 116392–116414. https://doi.org/10.1109/ACCESS.2020.2994561
  • Ariansyah, K., Setiawan, A. B., Hikmaturokhman, A., Ardison, A., & Walujo, D. (2024). Big data readiness in the public sector: An assessment model and insights from Indonesian local governments. Journal of Science and Technology Policy Management. Advance online publication. https://doi.org/10.1108/JSTPM-01-2023-0010
  • Attaran, M., Stark, J., & Stotler, D. (2018). Opportunities and challenges for big data analytics in US higher education: A conceptual model for implementation. Industry and Higher Education, 32(3), 169–182. https://doi.org/10.1177/0950422218770937
  • Baş, T. (2010). Anket. Ankara: Seçkin.
  • Berman, J. J. (2013). Principles of big data: Preparing, sharing, and analyzing complex information. Amsterdam: Elsevier, Morgan Kaufmann.
  • Brynjolfsson, E., & McAfee, A. (2013). Is your company ready for big data? Harvard Business Review. https://hbr.org/web/2013/06/assessment/is-your-company-ready-for-big-data adresinden 8 Mart 2022 tarihinde alınmıştır.
  • Casado, R., & Younas, M. (2015). Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience, 27(8), 2078–2091. https://doi.org/10.1002/cpe.3398
  • Chat GPT. (2023). OpenAI-ChatGPT. OpenAI. https://chat.openai.com adresinden 15 Ocak 2023 tarihinde alınmıştır.
  • Cheng, Y., & Zhang, S. (2023). Issues and countermeasures of information network security in the context of big data for higher education institutions. The Frontiers of Society, Science and Technology, 5(13). https://doi.org/10.25236/FSST.2023.051301
  • Choudhary, A. S. (2022). Hadoop ecosystem. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2022/10/hadoop-ecosystem adresinden 21 Ağustos 2024 tarihinde alınmıştır.
  • Claude, AI. (2024). Anthropic-Claude. Claude. https://claude.ai/new adresinden 21 Ekim 2024 tarihinde alınmıştır.
  • Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer Integrated Manufacturing, 62, 1–20. https://doi.org/10.1016/j.rcim.2019.101861
  • Dabhade, K. R. (2014). Big data: An overview. International Journal of Scientific & Technology Research, 3(10), 255–257.
  • Davenport, T. (2018). Big data @ work. Harvard Business School Publishing Corporation.
  • Dhayne, H., Chamoun, R. K., & Sokhn, M. (2018). Survey: When semantics meet crowdsourcing to enhance big data variety. In 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM) (pp. 1–6).
  • Ebner, K., Bühnen, T., & Urbach, N. (2014). Think big with big data: Identifying suitable big data strategies in corporate environments. In 2014 47th Hawaii International Conference on System Sciences (pp. 3748–3757).
  • Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732X20903304
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  • Ganeshkumar, C., Sankar, J. G., & David, A. (2023). Adoption of big data analytics: Determinants and performances among food industries. International Journal of Business Intelligence Research (IJBIR, 14(1), 1–17. https://doi.org/10.4018/IJBIR.317419
  • Gokulkumari, G. (2020). An overview of big data management and its applications. Resbee Publishers, 3(3), 10. https://doi.org/10.46253/jnacs.v3i3.a2
  • Gorbach, I., Shirokova, S., Bolsunovskaya, M., Leksashov, A., Shirokova, A., & Tsygan, V. (2021). Development of a BI application: Moving from a business idea to formulation of the problem. E3S Web of Conferences, 284, 04009. https://doi.org/10.1051/e3sconf/202128404009
  • Guha, P. (2018). Application of multivariate-rank-based techniques in clustering of big data. Vikalpa, 43(4), 179–190. https://doi.org/10.1177/0256090918804385
  • Guo, D., & Onstein, E. (2020). State-of-the-art geospatial information processing in NoSQL databases. ISPRS International Journal of Geo-Information, 9(5), 331. https://doi.org/10.3390/ijgi9050331
  • Gurcan, F., & Cagiltay, N. E. (2019). Big data software engineering: Analysis of knowledge domains and skill sets using LDA-based topic modeling. IEEE Access, 7, 82541–82552. https://doi.org/10.1109/ACCESS.2019.2924075
  • Hendrik, A. A., & Tjoa, A. M. (2014). Towards semantic mashup tools for big data analysis. In Linawati, M. S. Mahendra, E. J. Neuhold, A. M. Tjoa, & I. You (Eds.), Information and Communication Technology (pp. 129–138). Springer.
  • Hoang, C. T. T., Nhat, P. V., & Thi, H. T. D. (2024). Using the Delphi methodology to develop technology criteria to assess e-learning readiness in Higher education Institutions. Journal of Science and Technology, 7(1), 114-125. https://doi.org/10.55401/p0a42k67
  • Husamaldin, L., & Saeed, N. (2020). Big data analytics correlation taxonomy. Information, 11(1), 17. https://doi.org/10.3390/info11010017
  • IBM. (2022). Big data analytics. https://www.ibm.com/analytics/big-data-analytics adresinden 11 Ağustos 2022 tarihinde alınmıştır.
  • Jessup University. (2024). What is a cloud developer: Salary trends and skills needed for you to land the right job. https://jessup.edu/blog/engineering-technology/what-is-a-cloud-developer/ adresinden 21 Ekim 2024 tarihinde alınmıştır.
  • Jia, Z., Zhou, R., Zhu, C., Wang, L., Gao, W., Shi, Y., Zhan, J., & Zhang, L. (2014). The implications of diverse applications and scalable data sets in benchmarking big data systems. In Rabl, T., Poess, M., Baru, C., & Jacobsen, H.-A. (Eds.), Specifying Big Data Benchmarks (pp. 44–59). Springer.
  • Jones, K. M. L., McCoy, C., Crooks, R., & VanScoy, A. (2018). Contexts, critiques, and consequences: A discussion about educational data mining and learning analytics. Proceedings of the Association for Information Science and Technology, 55(1), 697–700. https://doi.org/10.1002/pra2.2018.14505501085
  • Kalita, J. K., Bhattacharyya, D. K., & Roy, S. (2024). Fundamentals of data science: Theory and practice. Academic Press.
  • Khan, S., Shakil, K. A., & Alam, M. (2016). Educational intelligence: Applying cloud-based big data analytics to the Indian education sector. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 29–34).
  • Khosla, M., Sheth, N., & Mahendra, M. S. (2018). Web application for providing immersive development & visualization of web pages. International Journal of Advanced Research in Computer Science, 9(3), 24–28. https://doi.org/10.26483/ijarcs.v9i3.5936
  • Kitchin, R., & McArdle, G. (2015). The diverse nature of big data.
  • Klievink, B., Romijn, B.J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 19(2), 267–283. https://doi.org/10.1007/s10796-016-9686-2
  • Kotha, S. (2023). Solving big data based on takeaways from experiences with small data.
  • Kourik, J. L., & Wang, J. (2017). The intersection of big data and the data life cycle: Impact on data management. International Journal of Knowledge Engineering, 3(2).
  • Kumar, N., Koneti, H. S. S., Hordiichuk, V., Menon, R., Aarthy, C. C. J., Saha, G. C., & Balaji, K. (2023). Harnessing the power of big data: Challenges and opportunities in analytics. Tuijin Jishu/Journal of Propulsion Technology, 44(2). https://doi.org/10.52783/tjjpt.v44.i2.193
  • Kumari, S. (2023). Big data: Navigating the Hadoop ecosystem: Unraveling the potential of big data. International Journal of Engineering & Technology, 12(1), 7–10. https://doi.org/10.14419/ijet.v12i1.32332
  • Mason, R. T. (2018). Changing paradigms of technical skills for data engineers. Issues in Informing Science and Information Technology, 15, 35–42.
  • McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review.
  • Microsoft Learn. (2024). Çözüm mimarı. https://learn.microsoft.com/tr-tr/training/career-paths/solution-architect adresinden 15 Ocak 2024 tarihinde alınmıştır.
  • Moshi, A., Sife, A., & Matto, G. (2024). The effect of awareness on big data adoption readiness in public sector auditing in Tanzania: Assessing TAM model. Asian Journal of Economics, Business and Accounting, 24(7), 341–354. https://doi.org/10.9734/ajeba/2024/v24i71414
  • Muhsin, T. F., Bhat, A. Z., & Samiulla Khan, I. A. M. (2020). Big data analytics for enhancing students experience in higher education – A case study. Journal of Student Research. https://doi.org/10.47611/jsr.vi.949
  • Murumba, J., & Micheni, E. (2017). Big data analytics in higher education: A review. The International Journal of Engineering and Science, 6(6), 14–21. https://doi.org/10.9790/1813-0606021421
  • Mustapha, I., Rattanawiboonsom, V., & Intanon, R. (2023). Data-driven insights in higher education: Exploring the synergy of big data analytics and mobile applications. International Journal of Interactive Mobile Technologies (iJIM, 17(20), 21–37. https://doi.org/10.3991/ijim.v17i20.45037
  • Nair, S. R. (2020). A review on ethical concerns in big data management. International Journal of Big Data Management, 1(1).
  • Nda, R. M., & Tasmin, R. B. (2019). Big data management in education sector: An overview. Path of Science, 5(6), 5009–5014. https://doi.org/10.22178/pos.47-6
  • Oracle. (2024). DevOps nedir?. https://www.oracle.com/tr/devops/what-is-devops/ adresinden 15 Ocak 2024 tarihinde alınmıştır.
  • Otto, K. M., & Lau, R. (2016). Leveraging big data to predict firms' performance. In Proceedings of the Fifth International Conference On Advances in Economics, Management and Social Study - EMS 2016 (pp. 47–50). Institute of Research Engineers and Doctors.
  • Ravikumar, R., Kitana, A., Taamneh, A., Aburayya, A., Shwedeh, F., Salloum, S., & Shaalan, K. (2023). The impact of big data quality analytics on knowledge management in healthcare institutions: Lessons learned from big data’s application within the healthcare sector. South Eastern European Journal of Public Health. https://doi.org/10.56801/seejph.vi.309
  • Ruaya, Emmer P., ve Mark Van Buladaco (2022). Virtual local area network (vlan) network design for nemsu- administration building. International Journal of Advanced Trends in Computer Science and Engineering, 11(6), 294-98. https://doi.org/10.30534/ijatcse/2022/101162022
  • Sadiku, M. N., Foreman, J., & Musa, S. M. (2018). Big data analytics: A primer. International Journal of Technologies and Management Research, 5(9), 44-49.https://doi.org/10.29121/ijetmr.v5.i9.2018.287
  • Safitri, Y. (2021). Key factors in big data implementation for smart city: A systematic literature review. JPAS (Journal of Public Administration Studies), 6(1), 16-22. https://doi.org/10.21776/ub.jpas.2021.006.01.3
  • Sander, I. (2020). What is critical big data literacy and how can it be implemented?. Internet Policy Review, 9(2), 1-22. https://hdl.handle.net/10419/218936
  • Saydullaev, S. (2023). Exploring big data applications for knowledge management in higher education administration”. Yashil Iqtisodiyot Va Taraqqiyot, 1(11-12), 936-943. https://doi.org/10.55439/GED/vol1_iss11-12/a374
  • Sonteya, T., & Seymour, L. F. (2012). Towards an understanding of the business process analyst: An analysis of competencies. Journal of Information Technology Education: Research, 11(1), 43-63.
  • Sun, Z., & Huo, Y. (2021). The spectrum of big data analytics. Journal of Computer Information Systems, 61(2), 154-62. https://doi.org/10.1080/08874417.2019.1571456.
  • Syed, S., & Albalawi, E. M. (2024). Harnessing big data and data science for enhanced efficiency in higher education: An exhaustive review and assessment. https://doi.org/10.21203/rs.3.rs-4283540/v1
  • Tan, S. S. L., G. Gao, ve S. Koch. 2018. “Big Data and Analytics in Healthcare”. Methods of Information in Medicine 54:546-47. https://doi.org/10.3414/ME15-06-1001
  • Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big Data, 2(1), 21. https://doi.org/10.1186/s40537-015-0030-3
  • Ujang, S., Saad, Z. A., Mohamad, M., Abdullah, M. A., & Sarimin, S. N. (2023). Assessing the Readiness of Staff at Uitm Pahang Toward Big Data Adoption. https://doi.org/10.21203/rs.3.rs-2663587/v1
  • Wang, Q., Jalil, H. A., & Marof, A. M. (2022). Factors affecting the acceptance of big data technology in teaching among higher education educators: An empirical investigation using the UTAUT model. International Journal of Academic Research in Business and Social Sciences, 12(12), 1049-1066. https://doi.org/10.6007/IJARBSS/v12-i12/15356
  • Wiese, D., & Rabinovitch G. (2009). Knowledge management in autonomic database performance tuning. In 2009 Fifth International Conference on Autonomic and Autonomous Systems, (pp. 129-134).
  • Xue, S. (2024). The application of big data technology in the analysis of higher education optimization management. Proceedings of the 3rd International Conference on New Media Development and Modernized Education, NMDME. http://dx.doi.org/10.4108/eai.13-10-2023.2341144
  • Yükseköğretim Kurulu Başkanlığı (2022). Yükseköğretim Kurulu-Büyük Veri Projesi.
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme , İş Sistemleri (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Hüseyin Şatırer 0000-0003-1334-4403

Ahmet Sait Özkul 0000-0001-8858-4685

Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 12 Kasım 2024
Kabul Tarihi 30 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 3 Sayı: 2

Kaynak Göster

APA Şatırer, H., & Özkul, A. S. (2024). YÜKSEKÖĞRETİM KURUMLARININ BÜYÜK VERİYE HAZIRLIĞI. Süleyman Demirel Üniversitesi İnsan Kaynakları Yönetimi Dergisi, 3(2), 119-140.