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The Growing Role of Big Data in Education and its Implications for Educational Leadership
Open accessAbstract
The big data technologies in education have seen a rapid rise since the past two decades.Information intelligence has become an integral part of educational decision making.This created a trend for the strategic usage of big amount of data.New technologies have been in use for analyzing the large amount of data for making strategic decisions.This study highlights the growing importance of big data by reviewing the existing literature on big data.Furthermore using interpretive methodology this study examines the implications of big data for educational leadership.Through effective decision making by using this big data enables educational institutes to improve the process of teaching and learning.The usage of big data enables effective decision making for education by incorporating different information and communication technologies.This Big data usage in education will intensify in the near future.Educational leaders need to build different learning management system to effectively utilize the big data for decision making purposes.
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“academic administrators increasingly recognize the importance of data-driven approaches in executing tasks effectively, ensuring prudent resource allocation, and strategic planning (Kalim, 2021).”
From Data-Driven Decision-Making through Real-time Student Progress Monitoring: Academic Administrators Perspectives (2025) · cites this paper as Kalim, 2021
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Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066 · cited 1×
“This new dimension of data has given business and other industries a new direction to bring efficiency and effectiveness in their functions by using petabyes of data (Sivarajah, Kamal, Irani, & Weerakkody, 2017).”
Background
Research works printed but not linked: 31
- Agnihotri, L., & Ott, A. (2014, July). Building a student at-risk model: An end-to-end perspective from user to data scientist. Proceedings of the 7th International Conference on Educational Data Mining, pp. 202-212.
- Anshari, M., Alas, Y., & Yunus, N. (2019). A survey study of smart phones behavior in Brunei: A proposal of Modelling big data strategies. In Multigenerational Online Behavior and Media Use: Concepts, Methodologies, Tools, and Applications, (pp. 201- 214). IGI global.
- Beheshti, B., & Desmarais, M. (2014, July). Predictive performance of prevailing approaches to skills assessment techniques: Insights from real vs. synthetic data sets. In Educational Data Mining 2014. Proceedings of the 7th International Conference on Educational Data Mining, pp. 409- 410.
- Bollier, D., & Firestone, C. M. (2010). The promise and peril of big data (pp. 1-66). Washington, DC: Aspen Institute, Communications and Society Program.
- Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of "big data"? Report, McKinsey Global Institute. October, San Francisco, CA. vol. 4, 24-35.
- Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey quarterly, 56(1), 75-86.
- Chae, B. K. (2019). A general framework for studying the evolution of the digital innovation ecosystem: The case of big data. International Journal of Information Management, 45(1), 83-94.
- Drigas, A. S., & Leliopoulos, P. (2014). The use of big data in education. International Journal of Computer Science Issues (IJCSI), 11(5), 58-66.
- Gento, S., Huber, G. L., González, R., Palomares, A., & Orden, V. J. (2015). Promoting the quality of educational institutions by enhancing educational leadership. US-China Education Review, 5(4), 215-232.
- Grafsgaard, J., Wiggins, J., Boyer, K. E., Wiebe, E., & Lester, J. (2014, July). Predicting learning and affect from multimodal data streams in task-oriented tutorial dialogue. In Educational Data Mining 2014. Proceedings of the 7th International Conference on Educational Data Mining.
- Herschel, R., & Miori, V. M. (2017). Ethics & big data. Technology in Society, 49(4), 31-36.
- Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36-44.
- Kalaian, S. A., Kasim, R. M., & Kasim, N. R. (2019). Descriptive and predictive analytical methods for big data. In Web Services: Concepts, Methodologies, Tools, and Applications, (pp. 314-331). USA: IGI global. https://doi.org/10.4018/978-1-5225-7501-6. ch018. · DOI
- Kellen, V., Recktenwald, A., & Burr, S. (2013). Applying Big Data in higher education: A case study. Cutter Consortium white paper, 13(8), 1-39.
- Khan, S., & Alqahtani, S. (2020). Big Data Application and its Impact on Education. International Journal of Emerging Technologies in Learning (iJET), 15(17), 36-46.
- Leithwood, K., Patten, S., & Jantzi, D. (2010). Testing a conception of how school leadership influences student learning. Educational Administration Quarterly, 46(5), 671-706.
- Logica, B., & Magdalena, R. (2015). Using big data in the academic environment. Procedia Economics and Finance, 33(4), 277-286.
- McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
- Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.
- Mohammad Hassan Falakmasir and Jafar Habibi, "Using educational data mining methods to study the impact of virtual classroom in e-learning", Proceedings of the 3 rd International Conference on Educational Data Mining, pp. 241-248, 2010.
- Mulford, B. & Silins, H. (2003). Leadership for organisational learning and student outcome-What do we know? Cambridge Journal of Education, 33(2), 175-195.
- Northhouse, P. G. (2013). Leadership: Theory and Practice (6th ed.). New Delhi: Sage Publications India Private Limited.
- Paulo Blikstein, "Using learning analytics to assess students' behavior in open-ended programming tasks", Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 110-116, 2011.
- 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.
- Seong Jae Lee, Yun-En Liu and Zoran Popovic, "Learning Individual Behavior in an Educational Game: A Data Driven Approach", Proceedings of the 7th International Conference on Educational Data Mining, 2014.
- Sergiovanni, T. J. (2004). What's in it for schools? Abingdon, Oxon: Routledge Falmer.
- Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 1-2.
- Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30-36.
- Sin, K., & Muthu, L. (2015). Applicaion of Big Data in education data minng and learning analytics. ICTACT journal on soft computing, 5(4), 1035-1049.
- Ur Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial internet of things. Future Generation Computer Systems, 92(2), 578-588.
- West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance studies at Brookings, 4(1), 1-10.
Cited by
Sorted by citations
Secreto, P. V., Ofrin, D., & Tabo, E. (2025) · International Journal in Information Technology in Governance, Education and Business · doi:10.32664/ijitgeb.v7i1.147 · Cited by 2
“Moreover, as key players in seamless institutional operations, academic administrators increasingly recognize the importance of data-driven approaches in executing tasks effectively, ensuring prudent resource allocation, and strategic planning (Kalim, 2021).”
Metadata
- Authors
- Kalim, U.
- Affiliations
- Southwest University (CN)
- Corresponding author
- Kalim, Usama
- ORCID
- Kalim, Usama
- Venue
- International Journal of Research and Innovation in Social Science
- Published
- 2021
- Volume
- 05
- Issue
- 01
- Pages
- 257-262
- ISSN
- 2454-6186 (ISSN-L)
- OpenAlex
- W3129171563
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 4 · 2022: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Research and Innovation in Social Science (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
- Keywords
- big data in education, educational leadership, educational decision-making, data-driven decision making, virtual learning environments, information and communication technologies, teaching and learning improvement
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