Data-Driven Decision Making in Education: Implications for Student Achievement and School Improvement
DOI:
https://doi.org/10.63948/intraj.v2i1.1957Keywords:
Data-Driven Decision-Making (DDDM), Student Achievement, Educational Leadership, Data Literacy, School ImprovementAbstract
Abstract - Data-driven decision making (DDDM) has emerged as a crucial methodology for enhancing instructional practices and student learning outcomes in educational environments. With enhanced access to student performance data, educators and administrators may make evidence-based decisions that promote academic success. This paper aims to analyze the correlation between data-driven decision-making (DDDM) and student academic performance, as well as to identify the elements that affect the effective application of data-informed practices in educational institutions. The essay examines the roles of teacher cooperation, data literacy, leadership support, and instructional interventions in facilitating efficient data utilization through a review and synthesis of recent work. Research demonstrates that the systematic application of educational data can enhance instructional preparation, focus student interventions, and foster favorable academic results, provided there is sufficient professional development and organizational backing. Nonetheless, obstacles such as inadequate training, insufficient resources, fragile data systems, and inconsistent execution may diminish the efficacy of DDDM programs. The paper asserts that data alone does not enhance student accomplishment; instead, significant interpretation, collaborative analysis, and instructional action are crucial for converting data into enhanced learning outcomes. Implications are presented for educational leadership, policy formulation, school enhancement initiatives, and prospective research.
References
Alsbou, M. K. K., & Alsaraireh, R. E. A. I. (2024, April). Data-driven decision-making in
education: Leveraging AI for school improvement. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE. https://doi.org/10.1109/ICKECS61492.2024.10616616
Alshamsi, A. M., El-Kassabi, H., Serhani, M. A., & Bouhaddioui, C. (2023). A multi-criteria
decision-making (MCDM) approach for data-driven distance learning recommendations. Education and Information Technologies, 28(8), 10421-10458. https://doi.org/10.1007/s10639-023-11589-9
Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A. (2025). Predicting student
performance and enhancing learning outcomes: a data-driven approach using educational data mining techniques. Computers, 14(3), 83. https://doi.org/10.3390/computers14030083
Botvin, M., Hershkovitz, A., & Forkosh-Baruch, A. (2023). Data-driven decision-making in
emergency remote teaching. Education and Information Technologies, 28(1), 489-506. https://doi.org/10.1007/s10639-022-11176-4
Bressane, A., Zwirn, D., Essiptchouk, A., Saraiva, A. C. V., de Campos Carvalho, F. L.,
Formiga, J. K. S., ... & Negri, R. G. (2024). Understanding the role of study strategies and learning disabilities on student academic performance to enhance educational approaches: A proposal using artificial intelligence. Computers and Education: Artificial Intelligence, 6, 100196. https://doi.org/10.1016/j.caeai.2023.100196
Chigbu, B. I., & Makapela, S. L. (2025). Data-driven leadership in higher education: advancing
sustainable development goals and inclusive transformation. Sustainability, 17(7), 3116. https://doi.org/10.3390/su17073116
Duong, H. T. H., Tran, L. T. M., To, H. Q., & Van Nguyen, K. (2023). Academic performance
warning system based on data driven for higher education. Neural Computing and Applications, 35(8), 5819-5837. https://doi.org/10.1007/s00521-022-07997-6
Ellikkal, A., & Rajamohan, S. (2025). AI-enabled personalized learning: empowering
management students for improving engagement and academic performance. Vilakshan-XIMB Journal of Management, 22(1), 28-44. https://doi.org/10.1108/XJM-02-2024-0023
Elugbaju, W. K., Okeke, N. I., & Alabi, O. A. (2024). Conceptual framework for enhancing
decision-making in higher education through data-driven governance. Global Journal of Advanced Research and Reviews, 2(02), 016-030. https://doi.org/10.58175/gjarr.2024.2.2.0055
Hebbecker, K., Förster, N., Forthmann, B., & Souvignier, E. (2022). Data-based decision-
making in schools: Examining the process and effects of teacher support. Journal of Educational Psychology, 114(7), 1695. https://psycnet.apa.org/doi/10.1037/edu0000530
Kaspi, S., & Venkatraman, S. (2023). Data-driven decision-making (DDDM) for higher
education assessments: A case study. Systems, 11(6), 306. https://doi.org/10.3390/systems11060306
Kustitskaya, T. A., Esin, R. V., Kytmanov, A. A., & Zykova, T. V. (2023). Designing an
education database in a higher education institution for the data-driven management of the educational process. Education Sciences, 13(9), 947. https://doi.org/10.3390/educsci13090947
Lin, L., Zhou, D., Wang, J., & Wang, Y. (2024). A systematic review of big data driven
education evaluation. Sage Open, 14(2), 21582440241242180. https://doi.org/10.1177/21582440241242180
Maniyan, S., Ghousi, R., & Haeri, A. (2024). Data mining-based decision support system for
educational decision makers: Extracting rules to enhance academic efficiency. Computers and Education: Artificial Intelligence, 6, 100242. https://doi.org/10.1016/j.caeai.2024.100242
Munagandla, V. B., Dandyala, S. S. V., & Vadde, B. C. (2024). Improving educational outcomes
through data-driven decision-making. International Journal of Advanced Engineering Technologies and Innovations, 3(1), 698-718.
Schildkamp, K., Poortman, C. L., Ebbeler, J., & Pieters, J. M. (2019). How school leaders can
build effective data teams: Five building blocks for a new wave of data-informed decision making. Journal of Educational Change, 20, 283–325. https://doi.org/10.1007/s10833-019-09345-3
Secreto, P. V., Ofrin, D., & Tabo, E. (2025). Data-Driven Decision-Making through Real-time
Student Progress Monitoring: Academic Administrators Perspectives. International Journal in Information Technology in Governance, Education and Business, 7(1), 97-115. https://doi.org/10.32664/ijitgeb.v7i1.147
Streifer, P. A., & Schumann, J. A. (2024). Using data mining to identify actionable information:
Breaking new ground in data-driven decision making. In Transforming Data Into Knowledge (pp. 281-293). Routledge.
Yağcı, M. (2022). Educational data mining: prediction of students' academic performance using
machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
Yu, L., & Shen, J. (2022). Analysis of the Correlation between Academic Performance and
Learning Motivation in English Course under a Corpus‐Data‐Driven Blended Teaching Model. Scientific Programming, 2022(1), 3407270. https://doi.org/10.1155/2022/3407270
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with INTRA: Interdisciplinary Research & Analysis Journal retain full copyright of their work. By submitting and publishing in INTRA, authors grant the journal the right of first publication and permission to archive and distribute the work under the journal’s licensing terms.
Unless otherwise stated, all content published in INTRA is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows others to share, adapt, and build upon the work—even for commercial purposes—provided appropriate credit is given to the original authors and the source.
For any questions regarding licensing or reuse, please contact: editorial@intraj.org
