Evaluation of Big Data Awareness and Educational Expectations of Faculty Members

Abstract

The aim of this study is to determine the awareness of the academicians about big data field and their expectations about education. In this direction, the software that academicians are familiar with and use about big data, their academic interests, and their expectations about education are revealed. The data were provided by 153 academicians in Computer Science, Mathematics, Statistics and Management Information Systems departments of universities in Ankara. For the analysis of the survey data, t-test analysis was used with SPSS 20.0 software. According to the findings, big data software that academicians are most familiar with is Oracle Big Data, Apache Hadoop, Microsoft Hadoop and Spark. The most used big data softwares are Apache Hadoop, Oracle Big Data, Spark and HDFS. The academic interest of faculty members in big data has been demonstrated by their academic studies that they have completed or are still continuing. Considering that the number of ongoing projects is more than twice the number of completed projects, it is understood that the academic interest of academicians in the field of big data has increased. The educational expectations, mean and standart deviation values of the expressions in the survey about big data are shared with results of the single sample t-test.

Keywords

• Big Data
• Big Data Awareness
• Higher Education
• Education Expectations

Öğretim Elemanlarının Büyük Veri Farkındalık ve Eğitim Beklentilerinin Değerlendirilmesi

Öz

Bu çalışmanın amacı, öğretim elemanlarının büyük veri alanı ile ilgili farkındalık ve eğitimle alakalı beklentilerinin değerlendirilerek belirlenmesidir. Bu doğrultuda öğretim elemanlarının büyük veri ile ilgili, aşina oldukları ve kullandıkları yazılımları, akademik ilgileri ve eğitimle alakalı beklentileri ortaya konulmuştur. Araştırmaya, Ankara ilinde belirlenen üniversitelerin Bilgisayar Mühendisliği, Matematik, İstatistik ve Yönetim Bilişim Sistemleri bölümlerinde ders veren 153 öğretim elemanı veri sağlamıştır. Anket verilerinin analizi için SPSS 20.0 yazılımı ile veri analizi yöntemlerinden t-testi analizi kullanılmıştır. Elde edilen bulgulara göre, öğretim elemanları tarafından en çok aşina olunan büyük veri yazılımları Oracle Big Data, Apache Hadoop, Microsoft Hadoop ve Spark, en çok kullanılan yazılımlar ise sırayla Apache Hadoop, Oracle Big Data, Spark ve HDFS olmuştur. Öğretim elemanlarının büyük veriyle ilgili akademik ilgilenimleri, tamamlamakta oldukları veya halen devam eden akademik çalışmaları üzerinden gösterilmiştir. Akademisyenlerin, devam eden proje sayısının, tamamlanmış proje sayısının iki katından fazla olduğu göz önünde bulundurulduğunda büyük veri alanına olan akademik ilgilerinin arttığı anlaşılmaktadır. Büyük veri ile ilgili eğitim beklentileri ise, anket formunda yer alan ifadelerin ortalama ve standart sapma değerleri tek örneklem t-testi sonuçlarıyla birlikte paylaşılmıştır.

Anahtar Kelimeler

• Büyük Veri
• Büyük Veri Farkındalığı
• Yükseköğretim
• Eğitim Beklentisi

Kaynakça

1. ACM and IEEE. (2005). Computing Curricula 2005, The Overview Report. The Association for Computing Machinery (ACM) and The Computer Society (IEEE-CS), 1-56.
2. ACM and IEEE. (2013). Computer Science Curricula 2013, Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. ACM and IEEE CS, 1-514.
3. ACM and IEEE. (2020). Computing Curricula 2020, A Computing Curricula Series Report Version 44. ACM and IEEE CS, 1-207.
4. Anshari, M., Alas, Y., Sabtu, N. P. H., & Hamid, M. S. A. (2016). Online Learning: trends, issues and challenges in the Big Data Era. Journal of e-Learning and Knowledge Society, 12(1), 121-134.
5. AYTAÇ, Z., & BİLGE, H. Ş. (2020). Big data analytics in higher education: a systematic review. Journal of Internet Applications and Management, 11(2), 81-99.
6. BachelorsPortal. (2021). Bachelor Degrees. URL: https://www.bachelorsportal.com/search/#q=di-282|lv-bachelor Son Erişim Tarihi: 12.01.2021
7. Beyer, M.A. & Laney, D., (2012). The Importance of “Big Data”: A Definition. Gartner Publications, pp.1–9.
8. Boyd, D. & Crawford, K., (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), pp.662–679.

9. Chen, H., Chiang, R. & Storey, V., (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), pp.1165–1188.
10. Cukier, K. (2010). Data, data everywhere. (2010, Feb 27), The Economist.
11. DataScience. (2019). datascience.community/. 2020 tarihinde datascience.community/: http://datascience.community/colleges adresinden alındı
12. DataScience. (2019). Data Science Colleges and Universities. URL: http://datascience.community/colleges Son Erişim Tarihi: 14.03.2020
13. Davis, K., and Patterson, D. (2012). Ethics of Big Data: Balancing risk and innovation. O'Reilly Media., 4.
14. De Mauro, A., Greco, M., and Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP conference proceedings, American Institute of Physics, 1644(1), 97-104.
15. Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (2013). Addressing big data issues in scientific data infrastructure. Paper presented at 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 48-55.
16. DeVellis, R. (2003). Scale development theory and applications (Second Edition). Sage Publications.
17. Dijcks, J., (2012). Oracle: Big data for the enterprise. Oracle White Paper.
18. Dontha, R. (2017). Who came up with the name Big Data. Data Science Central, 13.
19. Dumbill, E., (2013). Making Sense of Big Data. Big Data.
20. Fisher, D. vd., (2012). Interactions with Big Data Analytics. interactions.
21. Gahi, Y., Guennoun, M., and Mouftah, H. T. (2016). Big data analytics: Security and privacy challenges. Paper presented at 2016
22. IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 952-957.
23. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., and Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
24. Intel, (2012). Big Data Analytics. Intel’s IT Manager Survey on How Organizations Are Using Big Data.
25. İstanbul Teknik Üniversitesi. (2020). Yapay Zeka ve Veri Mühendisliği Ders Planı. URL: http://www.sis.itu.edu.tr/tr/dersplan/plan/YZVE/000000.html Son Erişim Tarihi: 25.06.2020
26. Krejcie, R. V., and Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.
27. Kurtuluş, K. (2010). Araştırma Yöntemleri. Türkmen Kitabevi. İstanbul, 181.
28. Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
29. Laney, D. (2012). Deja VVVu: others claiming Gartner’s construct for big data. Gartner Blog, 14.
30. Lawler, J., and Molluzzo, J. C. (2015). A proposed concentration curriculum design for big data analytics for information systems students. Information Systems Education Journal, 13(1), 45.
31. Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge. Research and Practice in Assessment.
32. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
33. Marzullo, K. (2016). Administration Issues Strategic Plan For Big Data Research and Developement.(May 23, 2016), The White House President Barack Obama.
34. Mayer-Schönberger, V. & Cukier, K., (2013). Big Data: A Revolution That Will Transform How We Live. Work and Think, London: John Murray.
35. Microsoft, (2013). The Big Bang: How the Big Data Explosion Is Changing the World.
36. National Science Foundation. (2019, 2 13). Big Data Science and Engineering Program. URL: https://www.nsf.gov/pubs/2019/nsf19039/nsf19039.jsp Son Erişim Tarihi: 27.06.2020
37. NIST Big Data Public Working Group, (2014). Big Data Interoperability Framework: Definitions (draft). Press, G. (2013). A Very Short History Of Big Data. (2013, May 09), Forbes.
38. Rajendran, P. K., Asbern, A., Kumar, K. M., Rajesh, M., and Abhilash, R. (2016). Implementation and analysis of MapReduce on biomedical big data. Indian Journal of Science and Technology, 9(31), 1-6.
39. Rydning, D. R. J. G. J. (2018). The digitization of the world from edge to core. Framingham: International Data Corporation.
40. Schroeck, M. vd., (2012). Analytics: The real-world use of big data.
41. Shneiderman, B., (2008). Extreme visualization: squeezing a billion records into a million pixels. International conference on Management of data, pp.3–12.
42. Siemens, G., and Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
43. Singh, D. S., and Singh, G. (2017). Big Data-A Review. International Research Journal of Engineering and Technology, 4(04), 2395-0056.
44. Smith, A., & Rose, R. (2002). Essential Elements: Prepare, Design, and Teach Your Online Course. Montreal: Association for the Advancement of Computing in Education (AACE).
45. Suthaharan, S., (2013). Big Data Classification: Problems and challenges in network intrusion prediction with machine learning. Big Data Analytics workshop.
46. Trifu, M. R., and Ivan, M. L. (2014). Big Data: present and future. Database Systems Journal, 5(1), 32-41.
47. Türk Dil Kurumu. (2019). Türk Dil Kurumu Sözlükleri URL: https://sozluk.gov.tr Son Erişim Tarihi: 27.12.2019
48. Tulasi, B. (2013). Significance of big data and analytics in higher education. International Journal of Computer Applications, 68(14), 23–25.
49. Warden, P. (2011). Big Data Glossary. O’Reilly Media, 1-60.
50. Ward, J. & Barker, A., (2013). Undefined By Data: A Survey of Big Data Definitions. arXiv preprint arXiv:1309.5821.
51. Wormer, P. V. (2014, 11 11). A sense of urgency: Excecutives rush to adobt Big Data analytics. URL: http://info.totaltraxinc.com/blog/a-sense-of-urgency-executives-rush-to-adopt-big-data-analytics Son Erişim Tarihi: 15.02.2021
52. YÖK Atlas. (2020). Yapay Zeka Mühendisliği Programı Bulunan Tüm Üniversiteler. URL: https://yokatlas.yok.gov.tr/lisans-bolum.php?b=554009 Son Erişim Tarihi: 10.11.2020
53. YÖK Atlas. (2020). YÖK Lisans Atlası. URL: https://yokatlas.yok.gov.tr/lisans-anasayfa.php Son Erişim Tarihi: 10.11.2020