การนำ AI และ Big Data มาช่วยในการตัดสินใจของผู้บริหารสถานศึกษา  ความเป็นไปได้และข้อท้าทาย

ธนัฐพร กำลังหาญ

Authors

ธนัฐพร กำลังหาญ -

Keywords:

Educational administration, Artificial Intelligence, Decision-making

Abstract

In the digital era, data has become a crucial component in driving development across all sectors, including education. School administrators must adapt by leveraging technological tools to support more effective decision-making. This article aims to explore the application of Artificial Intelligence (AI) and Big Data in educational administration and analyze the feasibility and challenges of such technologies within the Thai education context. Based on a review of relevant literature and theoretical frameworks, the study finds that AI and Big Data possess strong potential to support strategic decision-making processes in schools—for example, analyzing student achievement, predicting at-risk behaviors, allocating resources efficiently, and monitoring professional development for teachers. However, critical challenges remain, including inadequate infrastructure, limited personnel capacity, ethical concerns, and insufficient budget allocation. This article proposes four key strategies to promote sustainable implementation of AI and Big Data in school management: (1) enhancing digital competencies of school administrators and teachers, (2) developing technological infrastructure within schools, (3) establishing ethical data use policies, and (4) fostering collaboration with private sectors and higher education institutions. These recommendations aim to strengthen data-informed decision-making capacity and prepare schools to become innovative organizations in the digital age.

References

กระทรวงดิจิทัลเพื่อเศรษฐกิจและสังคม. (2565). รายงานประจำปี 2565. กรุงเทพฯ: กระทรวงดิจิทัลเพื่อเศรษฐกิจและสังคม.

สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน. (2565). รายงานการพัฒนาแพลตฟอร์มดิจิทัลเพื่อการศึกษาไทย. กรุงเทพฯ: สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน.

สำนักงานส่งเสริมเศรษฐกิจดิจิทัล. (2566). แผนพัฒนาดิจิทัลเพื่อเศรษฐกิจและสังคมแห่งชาติ ฉบับที่ 3 (พ.ศ. 2566–2570). กรุงเทพฯ: สำนักงานส่งเสริมเศรษฐกิจดิจิทัล.

Chai, C. S., & Kong, S. C. (2017). Professional learning for 21st century education. Journal of Computers in Education, 4(1), 1–4.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps in the study of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002.

Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920.

Fullan, M. (2013). The new meaning of educational change (4th ed.). Teachers College Press.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). The NMC Horizon Report: 2014 Higher Education Edition. The New Media Consortium.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. John Murray.

Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2010). Implementing data-informed decision making in schools: Teacher access, supports and use. U.S. Department of Education.

OECD. (2021). Digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing.

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.

Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union.

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355.

Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury.

Sheninger, E. (2019). Digital leadership: Changing paradigms for changing times (2nd ed.). Corwin.

Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78–81.

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 252–254). ACM.

Simon, H. A. (1947). Administrative behavior: A study of decision-making processes in administrative organizations. Macmillan.

Srivastava, P., & Khan, R. (2018). A review of cloud computing in education sector. International Journal of Advanced Research in Computer Science, 9(1), 224–227.

UNESCO. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO.

Williamson, B., & Piattoeva, N. (2022). Education governance and datafication. Oxford Research Encyclopedia of Education. https://doi.org/10.1093/acrefore/9780190264093.013.1743