Exploring Novel Educational Management Strategies in China's Big Data Era: A Theoretical Framework Analysis
Abstract
This paper examines the novel reaction methods used by Chinese universities for educational management in the age of big data. The four primary theoretical frameworks that are utilized are Bloom's Taxonomy, Gagne’s conditions of learning, Howard Gardner’s multiple intelligences, and the technology acceptance model. All things considered, these theories provide a firm foundation for studying and enhancing education nationwide. Both Bloom's Taxonomy and Gagne's conditions of learning contribute to a more rewarding educational experience by emphasizing the importance of learning objectives and levels in a hierarchical paradigm. Both the Technology Acceptance Model and Howard Gardner's idea of multiple intelligences acknowledge that students have different talents and learning styles. The goal of incorporating these theories is to create a more adaptable, technology-driven educational environment while simultaneously raising the bar for administration and instruction. The new big data era has brought new challenges to Chinese higher education, and this research hopes to help find better ways to deal with those challenges.
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Roh, Y., Heo, G., & Whang, S. E. (2019). A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328-1347. https://arxiv.org/pdf/1811.03402
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Mawgoud, A. A., Taha, M. H. N., & Khalifa, N. E. M. (2020). Security threats of social internet of things in the higher education environment. Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications: Emerging Technologies for Connected and Smart Social Objects, 151-171. https://www.researchgate.net/profile/Ahmed-A-Mawgoud/publication/334655853_Security_Threats_of_Social_Internet_of_Things_in_the_Higher_Education_Environment/links/5fb3796145851518fdacc332/Security-Threats-of-Social-Internet-of-Things-in-the-Higher-Education-Environment.pdf
Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, 57-65. https://www.rama.mahidol.ac.th/ceb/sites/default/files/public/pdf/Repository/Concurrence%20of%20big%20data%20analytics%20and%20healthcare_a%20systematic%20review.pdf
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://www.sciencedirect.com/science/article/pii/S014829631930061X
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and the moderating effect of the environment. British Journal of Management, 30(2), 272-298. https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2631308/Big%2BData%2BAnalytics%2BCapabilities%2BR2.pdf?sequence=1
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67. https://doi.org/10.4103%2Faca.ACA_157_18
Mishra, S. B., & Alok, S. (2022). Handbook of research methodology. ISBN: 978-1-5457-0340-3
Mncube, V., Mutongoza, B. H., & Olawale, E. (2021). Managing higher education institutions in the context of COVID-19 stringency: Experiences of stakeholders at a rural South African university. Perspectives in Education, 39(1), 390-409. https://journals.ufs.ac.za/index.php/pie/article/download/4606/4076
Mohammadpoor, M., & Torabi, F. (2020). Big Data analytics in oil and gas industry: An emerging trend. Petroleum, 6(4), 321-328. https://www.sciencedirect.com/science/article/pii/S2405656118301421
Muratov, H. (2021). The importance of organization and management independent education in the learning process. Збірник наукових праць ΛΌГOΣ. https://doi.org/10.36074/LOGOS-09.04.2021.V2.40
Mutlag, A. A., Abd Ghani, M. K., Arunkumar, N. A., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems, 90, 62-78. http://www.dl.edi-info.ir/Enabling%20technologies%20for%20fog%20computing%20in%20healthcare%20IoT%20systems.pdf
Neto, N. N., Madnick, S., Paula, A. M. G. D., & Borges, N. M. (2021). Developing a global data breach database and the challenges encountered. Journal of Data and Information Quality (JDIQ), 13(1), 1-33. https://dl.acm.org/doi/pdf/10.1145/3439873
Osadchy, E. A., Akhmetshin, E. M., Amirova, E. F., Bochkareva, T. N., Gazizyanova, Y., & Yumashev, A. V. (2018). Financial statements of a company as an information base for decision-making in a transforming economy. https://www.um.edu.mt/library/oar//handle/123456789/33582
Otterborn, A., Schönborn, K., & Hultén, M. (2019). Surveying preschool teachers' use of digital tablets: general and technology education-related findings. International journal of technology and design education, 29(4), 717-737. https://doi.org/10.1186/s41239-018-0130-1
Paul, P., & Aithal, P. S. (2018). Computing Academics into New Age Programs and Fields: Big Data Analytics & Data Sciences in Indian Academics—An Academic Investigation. IRA-International Journal of Management & Social Sciences, 10(3), 107-118. ISBN: 978-93-88879-95-8
Paz-Albo, J., Ruiz Ruiz, J. M., Bernárdez-Vilaboa, R., Huerta-Zavala, P., & Hervás-Escobar, A. (2021). The Impact of Socrative Exit Tickets on Initial Teacher Training. College Teaching, 1-9. https://www.tandfonline.com/doi/pdf/10.1080/87567555.2021.1971602
Pessoa, A. S. G., Harper, E., Santos, I. S., & Gracino, M. C. D. S. (2019). Using reflexive interviewing to foster a deep understanding of research participants' perspectives. International Journal of Qualitative Methods, 18, 1609406918825026. https://doi.org/10.1177/1609406918825026
Pumsirirat, A., & Liu, Y. (2018). Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. International Journal of advanced computer science and applications, 9(1). https://pdfs.semanticscholar.org/01be/7624aa0e0251182593350a984411c2e5128a.pdf
Quesado, P. R., Aibar Guzmán, B., & Lima Rodrigues, L. (2018). Advantages and contributions in the balanced scorecard implementation. Intangible capital, 14(1), 186-201. http://dx.doi.org/10.3926/ic.1110
Radha, R., Mahalakshmi, K., Kumar, V. S., & Saravanakumar, A. R. (2020). E-Learning during a lockdown of Covid-19 pandemic: A global perspective. International journal of control and automation, 13(4), 1088-1099. https://www.academia.edu/download/64029090/covid-pandemic-scopus.pdf
Ragini, J. R., Anand, P. R., & Bhaskar, V. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management, 42, 13-24. https://wiki.uib.no/info319/images/2/2f/P5-2019.pdf
Raja, R., Mukherjee, I., & Sarkar, B. K. (2020). A systematic review of big healthcare data. Scientific programming, 2020. https://www.hindawi.com/journals/sp/2020/5471849/
Roh, Y., Heo, G., & Whang, S. E. (2019). A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328-1347. https://arxiv.org/pdf/1811.03402
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://bookdown.org/chen/la-manual/files/Romero%20and%20Ventura%20-%202020.pdf
Published
2024-12-01
How to Cite
LIAOYUAN, Meng; TAHIR, Suhaidah.
Exploring Novel Educational Management Strategies in China's Big Data Era: A Theoretical Framework Analysis.
Asian Journal of Research in Education and Social Sciences, [S.l.], v. 6, n. 4, p. 151-159, dec. 2024.
Available at: <https://myjms.mohe.gov.my/index.php/ajress/article/view/28515>. Date accessed: 16 may 2025.
Section
English Section
