OPEN DATA AND BIG DATA MODEL FOR HIGHER EDUCATION AND POSSIBLE OUTCOMES

Abstract

The basic outputs of universities can be listed as education, research-development and service to society. Managerial software systems at universities generate large amount of open data during daily operations. The data generated by these systems contain valuable public institutional performance information along with critical private information. These public data can be classified, collected and processed by using big data approaches for performance monitoring. In this study, an open data platform is modelled, and issues are discussed related how open data is collected, stored and processed using big data approaches to extract interested performance information. It is shown that institutional performance information can be presented according to a wide variety of metrics from the collected data. Scientific studies that can be carried out in higher education using big data are examined under 4 headings: Creating an open data directive for universities, development of open data platform, institutional accreditation service, creating a digital twin. This platform can be used for online institutional evaluation either by university management or accreditation agencies.

Keywords

• Open data
• Big data
• Higher education

YÜKSEK ÖĞRENİMDE AÇIK VERİ VE BÜYÜK VERİ MODELİ VE OLASI SONUÇLARI

Abstract

Bir üniversitenin temel çıktıları eğitim, araştırma-geliştirme ve topluma hizmet olarak listelenebilir. Üniversitelerdeki yönetim yazılım sistemleri günlük işlemler sırasında büyük miktarda açık veri üretir. Bu sistemler tarafından üretilen veriler, kritik özel bilgilerle birlikte değerli kamu kurumsal performans bilgilerini içerir. Bu kamuya açık veriler, performans izleme için büyük veri yaklaşımları kullanılarak sınıflandırılabilir, toplanabilir ve işlenebilir. Bu çalışmada, bir açık veri platformu modellenmiş ve ilgili performans bilgilerinin çıkarılması için büyük veri yaklaşımları kullanılarak açık verilerin nasıl toplandığı, saklandığı ve işlendiği ile ilgili konular tartışılmıştır. Toplanan verilerden kurumsal performans bilgilerinin çok çeşitli ölçütlere göre sunulabileceği gösterilmiştir. Yükseköğretimde büyük veri kullanılarak yapılabilecek bilimsel çalışmalar 4 başlık altında incelenmektedir: Üniversiteler için açık veri yönergesi oluşturulması, açık veri platformunun geliştirilmesi, kurumsal akreditasyon hizmeti, dijital ikiz oluşturulması. Bu platform, gerek üniversite yönetimi gerekse akreditasyon kurumları tarafından çevrimiçi kurumsal değerlendirme için kullanılabilir.

Keywords

• Açık veri
• Büyük veri
• Yüksek öğretim

References

1. Thorsby, J., Stowers, G.N.L., Wolslegel, K., and Tumbuan, E., "Understanding the content and features of open data portals in American cities", Government Information Quarterly, vol. 34, no. 1, pp. 53-61, 2017.
2. Muenchen.de “Willkommen beim Open-Data-Portal München”, muenchen.de, Online. Available: https://opendata.muenchen.de/, Accessed: 05.10.2022.
3. NYC.gov, “Open Data for All New Yorkers”, NYC Open Data, Online. Available: https://opendata.cityofnewyork.us/, Accessed: 05.10.2022.
4. data.europa.eu, “Open Data Best Practices in Europe”, Online.Available: https://data.europa.eu/en/datastories/open-data-best-practices-europe, Accessed: 02.09.2022.
5. Sağıroğlu, Ş., Bensghir, T. and et. al. (Sağıroğlu, Ş., and Koç, O.), “Büyük Veri ve Açık Veri Analitiği: Yöntemler ve Uygulamalar”, Grafiker Yayınları, Ankara, 2017.
6. Drigas, A. S. and Leliopoulos, P., "The Use of Big Data in Education", IJCSI International Journal of Computer Science Issues, vol. 11, no. 5, 2014.
7. Bughin, J., Chui, M., and Manyika, J., "Clouds, big data, and smart assets: Ten tech-enabled business trends to watch", McKinsey Quarterly, vol. 56, pp. 75-86, 2010.
8. Kataev, M. and Bulysheva, L., "Blockchain system in the higher education: Storing academical students' records and achievements accumulated in the educational process", Systems Research and Behavioral Science, vol. 39, no. 3, pp. 589-596, 2022.

9. Park, J., "Promises and challenges of Blockchain in education", Smart Learning Environments, vol. 8, no. 1, pp. 33, 2021.
10. Lukić, J., "The Impact of Big Data Technologies on Competitive Advantage of Companies", Facta Universitatis Series: Economics and Organization, vol. 14, no. 3, pp. 255-264, 2017.
11. Shu, H., "Big data analytics: six techniques", Geo-spatial Information Science, 19(2), 119-128, 2016.
12. Open Foundation Knowledge, “What is open?” Available: https://okfn.org/opendata/, Accessed: 05.10.2022.
13. Gürdal, G., "Açık veri–açık erişim: Dünya ve Türkiye", Dünya ve Türkiye Bilişim Dergisi, pp. 104-109, 2014.
14. Akdamar E., “Akıllı Kent İdealine Ulaşmada Açık Verinin Rolü,” Sosyal Bilimler Araştırma Dergisi, vol. 6, no. 1, pp. 45–52, 2017.
15. Raffaghelli, J. E., "Open Data for Learning: A case study in Higher Education", Exploring the Micro, Meso and Macro Proceedings of the European Distance and E-Learning Network 2018 Annual Conference, 2018.
16. Atenas, J. and Havemann, L., "Open Data Sectors and Communities: Education", in The State of Open Data: Histories and Horizons, edited by Davies, T., Walker, S.B., Rubinstein, M., and Perini, F., African Minds, South Africa, 2019.
17. Atenas, J. and Havemann, L., "Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship", Open Praxis, Vol. 7, pp. 377-389, 2015.
18. Coughlan, T., "The use of open data as a material for learning", Educational Technology Research and Development, vol. 68, no. 1, pp. 383-411, 2020.
19. Toptaş, O. and Şen, A., "Eğitimde Ölçme Değerlendi̇rmede Büyük Veri̇ni̇n Önemi̇," Düşünce ve Toplum Sosyal Bilimler Dergisi, no. 4, pp. 223–243, 2021.
20. West D. M., “Big Data for Education Data Mining, Data Analytics and Web Dashboards”, 2012.
21. Siemens, G., and Gasevic, D., "Guest editorial - learning and knowledge analytics," Educational Technology and Society, vol. 15, no. 3, pp. 1-2, 2012.
22. Schalkwyk F. van, “Open data on universities – New fuel for transformation”, University world news, Online. Available: https://www.universityworldnews.com/post.php?story=20170710104034491, Accessed: 05.10.2022.
23. Dumontier M. and Wesley K., "Advancing Discovery Science with FAIR Data Stewardship: Findable, Accessible, Interoperable, Reusable", The Serials Librarian, vol. 74, no. 1-4, pp. 39-48, 2018.
24. F. Haupt, F. Leymann, A. Scherer and K. Vukojevic-Haupt, "A Framework for the Structural Analysis of REST APIs," 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden, 2017, pp. 55-58.
25. Halili F. and Ramadani E., "Web Services: A Comparison of Soap and Rest Services," Modern Applied Science, vol. 12, no. 3, pp. 175-182, 2018.
26. Sharvari, T., and Sowmya Nag, K., “A study on Modern Messaging Systems- Kafka, RabbitMQ and NATS Streaming”, 2019.
27. Fleischer H., “Applying event-driven architecture in the jvalue ods,” Master Thesis, 2020.
28. Hiraman, B. R., Viresh M. C., and Abhijeet K. C., "A Study of Apache Kafka in Big Data Stream Processing," 2018 International Conference on Information, Communication, Engineering and Technology (ICICET), Pune, India, 2018, pp. 1-3.
29. Singh, M., Fuenmayor, E., Hinchy, E. P., Qiao, Y., Murray, N., and Devine, D., “Digital Twin: Origin to Future”, Applied System Innovation, vol. 4, no. 2, pp. 1–20, 2021.
30. Gabrielli, S., Rizzi, S., Mayora, O., More, S., Pérez Baun, J.C., and Vandevelde, W., "Multidimensional Study on Users’ Evaluation of the KRAKEN Personal Data Sharing Platform", Applied Sciences, vol. 12, no. 7, pp. 3270, 2022.
31. Fuller, A., Fan, Z., Day, C., and Barlow, C., "Digital Twin: Enabling Technologies, Challenges and Open Research", IEEE Access, vol. 8, pp. 108952-108971, 2020.