Yükseköğretim’de büyük veri analitiği: sistematik bir literatür taraması

Year 2020, Volume: 11 Issue: 2, 81 - 99, 29.12.2020

Zeynep Aytaç , Hasan Şakir Bilge

Abstract

Eğitimde öğrenme teknikleri ve ortamları son bir kaç yıl içerisinde farklılaşmakta ve gelişmektedir. Çevrimiçi öğrenme ortamlarındaki etkinliklerden elde edilen veriler, büyük veri teknolojileri kullanılarak yükseköğrenimdeki iyileştirme ve geliştirme çalışmaları için önemli veri kaynakları oluşturmaktadır. Bu çalışma gelişmekte olan büyük veri analitiği alanının, yükseköğrenimde literatürün gözden geçirilmesine dayanmaktadır. Bu çalışmada, yükseköğretimde öğrenciler, eğitimciler, yöneticiler ve ders geliştiriciler olmak üzere dört paydaş grubu, büyük veri ve eğitimsel büyük veri analitiğinin kavramsal modeli kullanılarak tartışılmıştır. Ayrıca bu araştırmada farklı öğrenme ortamları da bir yapı içerisinde tartışılmıştır. Bu çalışmanın temel amacı, yüksek öğrenimde büyük veri analitiğinin hangi konulara daha çok yöneldiğini belirlemek için yüksek öğrenimde büyük veri analitiği ile ilgili yayınlanmış 40 makaleyi sistematik olarak incelemektir. Literatür taramasından elde edilen bulgulara dayanılarak, özellikle müfredat geliştirme çalışmaları incelenmiş ve kritik bulgular tartışılmıştır.

Keywords

büyük veri, yükseköğretim, öğrenme ortamları, müfredat geliştirme

Big data analytics in higher education: a systematic review

Abstract

The learning techniques and environment in education has been changing and developing in the last few years. The data obtained from the activities in online learning environments constitute an important data source for improvement and development studies in higher education using big data technologies. This study is based on a review of the literature that focused on the evolving area of big data analytics in higher education. Four groups of stakeholders, namely students, educators, administrators and course developers, in higher education are discussed in this study by utilizing big data and the conceptual model of educational big data analytics. We also discussed different learning environments in a framework in this research. The main objective of this study is to systematically review 40 published articles on big data analytics in higher education in order to determine the subjects of big data analytics in higher education. Based on the findings of the literature review, especially the curriculum development studies were examined and critical findings were discussed.

Keywords

big data, higher education, learning environments, curriculum development

References

• Altaye, A. A., & Nixon, J. S. (2019). A Comparative Study on Big Data Applications in Higher Education. International Journal of Emerging Trends in Engineering Research, 7(12), 739-745.
• 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.
• 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.
• Baiocchi, R. R. (2019). Exploring Data Driven Youth Character Education Frameworks: A Systematic Literature Review on Learning Analytics Models and Participatory Design. Estudios Sobre Educacion, 37, 179-198.
• Baker, R. S., & Yacef, K. (2009). The State of Educational Data Mining In 2009: A Review and Future Visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17.
• Bersin, J. (2018, 03 31). https://joshbersin.com. Retrieved 11 26, 2018, from https://joshbersin.com: https://joshbersin.com/2017/03/the-disruption-of-digital-learning-ten-things-we-have-learned/
• Bologa, R., Lupu, A. R., Boja, C., & Georgescu, T. M. (2017). Sustaining Employability: A Process for Introducing Cloud Computing, Big Data, Social Networks, Mobile Programming and Cybersecurity into Academic Curricula. Sustainability, 9(12), 2235.
• Chen, X., Self, J. Z., House, L., Wenskovitch, J., Sun, M., Wycoff, N., & North, C. (2017). Be the Data: Embodied Visual Analytics. IEEE Transactions on Learning Technologies, 11(1), 81-95.
• Daniel, B. (2015). Big Data and Analytics in Higher Education: Opportunities and Challenges. British Journal of Educational Technology, 46(5), 904-920.
• Daniel, B. K., & Butson, R. (2013). Technology Enhanced Analytics (TEA) in Higher Education. International Association for the Development of the Information Society, 89-96.
• Dapiton, E. P., & Canlas, R. B. (2020). Value Creation of Big Data Utilization: The Next Frontier for Productive Scholarship among Filipino Academics. European Journal of Educational Research, 9(1), 423-431.
• Esomonu, N. P. M., Esomonu, M. N., & Eleje, L. I. (2020). Assessment Big Data in Nigeria: Identification, Generation and Processing in the Opinion of the Experts. International Journal of Evaluation and Research in Education, 9(2), 345-351.
• Freeman, L. (2004). The Development of Social Network Analysis. A Study in the Sociology of Science, 1, 687, 159-167.
• Friedman, A. (2018). Measuring the Promise of Big Data Syllabi. Technology, Pedagogy and Education, 27(2), 135-148.
• Fynn, A. (2016). Ethical Considerations in the Practical Application of the Unisa Socio-Critical Model of Student Success. The International Review of Research in Open and Distributed Learning, 17(6).
• Gokul, K., Sundararajan, M., & Paul, P. (2019). Big Data Management, Data Science and Data Analytics: What is it and Where— An Educational in Indian Perspective. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12).
• Gupta, B., Goul, M., & Dinter, B. (2015). Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs. Communications of the Association for Information Systems, 36, Article 23.
• Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges. Journal of Big Data, 6(1), 44.
• Huda, M., Anshari, M., Almunawar, M. N., Shahrill, M., Tan, A., Jaidin, J. H., & Masri, M. (2016). Innovative Teaching in Higher Education: The Big Data Approach. TOJET, November, Special Issue for INTE 2016, 1210-1216.
• Huda, M., Maseleno, A., Atmotiyoso, P., Siregar, M., Ahmad, R., Jasmi, K., & Muhamad, N. (2018). Big Data Emerging Technology: Insights into Innovative Environment for Online Learning Resources. International Journal of Emerging Technologies in Learning (iJET), 13(1), 23-36.
• Huda, M., Maseleno, A., Teh, K. S. M., Don, A. G., Basiron, B., Jasmi, K. A., & Ahmad, R. (2018). Understanding Modern Learning Environment (MLE) in Big Data Era. International Journal of Emerging Technologies in Learning (iJET), 13(05), 71-85.
• IBM Software Group. (2001). Analytics for Achievement. Ottawa: IBM Canada.
• Johnson, L., Adams, S., & Cummins, M. (2012). The 2012 Horizon Report. Austin: The New Media Consortium.
• Johnson, L., Levine, A., Smith, R., & Stone, S. (2010). The 2010 Horizon Report. The New Media Consortium.
• Jones, K. M. (2019). Learning Analytics and Higher Education: A Proposed Model for Establishing Informed Consent Mechanisms to Promote Student Privacy and Autonomy. International Journal of Educational Technology in Higher Education, 16(1), 24.
• Khan, S., Liu, X., Shakil, K. A., & Alam, M. (2019). Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems. International Journal of Advanced Computer Science and Applications (IJACSA), 10(6), 292-304.
• Kim, D. R., Hue, J. P., & Shin, S. S. (2016). Application of Learning Analytics in University Mathematics Education. Indian Journal of Science and Technology, 9(46), 1-5.
• Klochkova, E., Serkina, Y., Prasolov, V., & Movchun, V. (2020). The Digitalisation of the Economy and Higher Education. Space and Culture, India, 7(4), 70-82.
• Lane, J. E., & Finsel, B. A. (2014). Fostering Smarter Colleges and Universities. Building a Smarter University: Big Data, Innovation, and Analytics, 3-26.
• Laux, C., Li, N., Seliger, C., & Springer, J. (2017). Impacting Big Data Analytics in Higher Education through Six Sigma Techniques. International Journal of Productivity and Performance Management, 66(5), 662-679.
• Li, Y., Huang, C., & Zhou, L. (2018). Impacts on Statistics Education in Big Data Era. Educational Sciences: Theory & Practice, 18(5), 1236-1245.
• Lodge, J. M., Alhadad, S. S., Lewis, M. J., & Gašević, D. (2017). Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach. Technology, Knowledge and Learning, 22(3), 385-400.
• Lohr, S. (2012, 02 11). The Age of Big Data. Big Data’s Impact in the World, pp. 1-5.
• Lu, J. (2020). Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum. INFORMS Transactions on Education, 20(2), 57-72.
• Macfadyen, L. P., Dawson, S., Pardo, A., & Gaševic, D. (2014). Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge. Research & Practice in Assessment, 9, 17-28.
• Mahroeian, H., & Daniel, B. K. (2016, June). The Dynamic Landscape of Higher Education: The Role of Big Data and Analytics. In EdMedia+ Innovate Learning (pp. 1320-1325). Association for the Advancement of Computing in Education (AACE).
• Margo, H. (2004). Data Mining in the E-Learning Domain. Campus-Wide Information Systems, 21(1), 29-34.
• McGill, T. J., & Klobas, J. E. (2009). A Task–Technology Fit View of Learning Management System Impact. Computers & Education, 52(2), 496-508.
• Mehmood, R., Alam, F., Albogami, N. N., Katib, I., Albeshri, A., & Altowaijri, S. M. (2017). UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies. IEEE Access, 5, 2615-2635.
• Moscoso-Zea, O., Castro, J., Paredes-Gualtor, J., & Luján-Mora, S. (2019). A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education. IEEE Access, 7, 38778-38788.
• Novak, J. D., & Cañas, A. J. (2008). The Theory Underlying Concept Maps and How to Construct and Use Them. Technical Report IHMC CmapTools, Florida.
• Picciano, A. G. (2012). The Evolution of Big Data and Learning Analytics in American Higher Education. Journal of asynchronous learning networks, 16(3), 9-20.
• Picciano, A. G. (2014). Big Data and Learning Analytics in Blended Learning Environments: Benefits and Concerns. IJIMAI, 2(7), 35-43.
• Prensky, M. (2003). Digital Game-Based Learning. Computers in Entertainment (CIE), 1(1), 21-21.
• Prinsloo, P., Archer, E., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big (Ger) Data as Better Data in Open Distance Learning. International Review of Research in Open and Distributed Learning, 16(1), 284-306.
• Reidenberg, J. R., & Schaub, F. (2018). Achieving Big Data Privacy in Education. Theory and Research in Education, 16(3), 263-279.
• Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State Of The Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
• Sedkaoui, S., & Khelfaoui, M. (2019). Understand, Develop and Enhance the Learning Process with Big Data. Information Discovery and Delivery, 47(1), 2-16.
• Shah, B., & Choksi, D. (2019). Big Data Analytics Model for the Education Sector. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12).
• Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the Power of Big Data Analytics in the Cloud to Support Learning Analytics in Mobile Learning Environment. Computers in Human Behavior, 92, 578-588.
• Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
• Singh, D. S., & Singh, G. (2017). Big data – A Review. International Research Journal of Engineering and Technology, 4(4), 822-824.
• Smith, A., & Rose, R. (2002). Essential Elements: Prepare, Design, and Teach Your Online Course. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2723-2724). Association for the Advancement of Computing in Education (AACE).
• Suhasini, B., & Kumar N., S. (2019). Emerging Trends and Future Perspective of Human Resource Reskilling in Higher Education. International Journal of Recent Technology and Engineering (IJRTE), 8(2S4).
• Swain, H. (2013, August 5). The Guardian. Retrieved October 15, 2018, from http://www.theguardian.com/education/2013/aug/05/electronic-data-trail-huddersfield-loughborough-university
• Tulasi, B., & Suchithra, R. (2019). Personalized Learning Environment in Higher Education through Big Data and Blended Learning Analytics. International Journal of Recent Technology and Engineering (IJRTE), 8(3).
• Van Eck, R. (2006). Digital game-based learning: It's not just the digital natives who are restless. EDUCAUSE review, 41(2), 16.
• Venkatraman, S., Overmars, A., & Wahr, F. (2019). Visualization and Experiential Learning of Mathematics for Data Analytics. Computation, 7(3), 37.
• Wang, Y. (2017). Education Policy Research in the Big Data Era: Methodological Frontiers, Misconceptions, and Challenges. Education Policy Analysis Archives, 25(94), 1-24.
• Wen, J., Zhang, W., & Shu, W. (2019). A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment. The Journal of Supercomputing, 75(2), 719-731.
• Whitman, M. (2020). “We Called That a Behavior”: The Making of Institutional Data. Big Data & Society, 7(1), 1-13.
• Zhang, G., Li, J., & Hao, L. (2015). Research on Cloud Computing and Its Application in Big Data Processing of Distance Higher Education. International Journal of Emerging Technologies in Learning, 10(8), 55-58.
• Zikopoulos, P., & Eaton, C. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media.