ARCHIVES
Review Article
A Review of Data-Driven Decision-Making Approaches in Curriculum Design
Dr. Deepali Y. Kirange1
Dr. Yogesh N. Chaudhari2
12Assistant Professor, KCES’s Institute of Management and Research, Jalgaon, Maharashtra, India.
Published Online: July-August 2025
Pages: 26-29
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250604004References
1. Schildkamp, K., & Kuiper, W. (2010). Data use in schools as a foundation for improving teaching and curriculum design. Systematic review.
2. Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for teachers and data-driven decision-making… Frontiers in Education.
3. Dimara, E., et al. (2021). Curriculum decisions based on data analysis tend to be more accurate... Data driven Educational Planning Strategy.
4. Yindrizal, & Susiana. (2024). Data Driven Educational Planning Strategy: Examining challenges… ResearchGate preprint.
5. Romero, C., & Ventura, S. (2024). Educational data mining and learning analytics: An updated survey. arXiv preprint.
6. Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2023). Integrating AI and learning analytics for data-driven pedagogical decisions… arXiv preprint.
7. Ifenthaler, D., & Yau, J. Y. K. (2024). Analyzing Learner Feedback and Educational Data for Curriculum Development.In D. Ifenthaler (Ed.), Data Analytics in Higher Education (pp. 23–37). Springer. https://doi.org/10.1007/978-3-031-54464-4_2
8. Kovanović, V., Gašević, D., & Siemens, G. (2024). Learning Analytics Methods and Tools. In R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Handbook of Learning Analytics (2nd ed., pp. 21–40). Society for Learning Analytics Research (SoLAR). https://lamethods.org/book1/chapters/ch02-data/ch2-data.html
9. Papamitsiou, Z., & Economides, A. A. (2022). Learning Analytics and Educational Data Mining in Practice: A Systematic Review of Empirical Evidence. Internet and Higher Education, 55, 100853. https://doi.org/10.1016/j.iheduc.2022.100853
10. Romero, C., Ventura, S., & Pechenizkiy, M. (2024). Educational Data Mining and Learning Analytics: An Updated Survey. Patterns, 5(3), 100663. https://doi.org/10.1016/j.patter.2024.100663
11. Pahl, C. (2004). Data generated by learner interactions with LMS supports learning analytics. Journal of Learning Analytics, 1(2), 94–125.
12. Kovanović, V., Gašević, D., & Siemens, G. (2023). Learning analytics methods and tools. In R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Handbook of Learning Analytics (2nd ed., pp. 21–40). SoLAR.
13. Kimball, R. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley.
14. Integrate.io. (2025). How to build data pipelines for the education industry. Integrate.io Blog.
15. SchoolAnalytix. (2024). Implementing a data warehouse system in education. SchoolAnalytix Blog.
16. Moreira, F., Machado, J., & Costa, C. J. (2022). A dataset for predicting student dropout and academic success. Data, 7(11), 146. https://doi.org/10.3390/data7110146
17. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. https://doi.org/10.1002/widm.1075
18. Sweeney, M., Lester, J., & Rangwala, H. (2016). Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining, 8(1), 22–51.
19. Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Matrix factorization vs. neighborhood models: Reducing the long tail of recommender systems. In Proceedings of the 2010 IEEE International Conference on Computer Science and Education (ICCSE) (pp. 1–5). IEEE.
20. Sarikaya, A., & Correll, M. (2017). Factors affecting visual analytics usage patterns. IEEE Transactions on Visualization and Computer Graphics, 24(1), 286–296. https://doi.org/10.1109/TVCG.2017.2745080
21. Zhang, J., Almeroth, K., Zheng, B., & Niemi, D. (2020). Using learning analytics dashboards to support self-regulated learning in online environments. Educational Technology Research and Development, 68, 1053–1076. https://doi.org/10.1007/s11423-020-09731-1
22. Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
23. Crawley, E. F., Malmqvist, J., Östlund, S., Brodeur, D. R., & Edström, K. (2014). Rethinking engineering education: The CDIO approach (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-05561-9
24. Spady, W. G. (1994). Outcome-Based Education: Critical Issues and Answers. American Association of School Administrators.
25. Daniel, B. K. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
26. Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
27. Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64. https://www.jstor.org/stable/jeductechsoci.17.4.49
28. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
29. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A Reference Model for Learning Analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318–331. https://doi.org/10.1504/IJTEL.2012.051815
30. Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938. https://doi.org/10.1007/s11423-016-9477-y
31. Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167–179. https://doi.org/10.1007/s10676-019-09519-7
32. Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014). Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems. Learning, Media and Technology, 39(1), 10–21. https://doi.org/10.1080/17439884.2013.861517
2. Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for teachers and data-driven decision-making… Frontiers in Education.
3. Dimara, E., et al. (2021). Curriculum decisions based on data analysis tend to be more accurate... Data driven Educational Planning Strategy.
4. Yindrizal, & Susiana. (2024). Data Driven Educational Planning Strategy: Examining challenges… ResearchGate preprint.
5. Romero, C., & Ventura, S. (2024). Educational data mining and learning analytics: An updated survey. arXiv preprint.
6. Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2023). Integrating AI and learning analytics for data-driven pedagogical decisions… arXiv preprint.
7. Ifenthaler, D., & Yau, J. Y. K. (2024). Analyzing Learner Feedback and Educational Data for Curriculum Development.In D. Ifenthaler (Ed.), Data Analytics in Higher Education (pp. 23–37). Springer. https://doi.org/10.1007/978-3-031-54464-4_2
8. Kovanović, V., Gašević, D., & Siemens, G. (2024). Learning Analytics Methods and Tools. In R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Handbook of Learning Analytics (2nd ed., pp. 21–40). Society for Learning Analytics Research (SoLAR). https://lamethods.org/book1/chapters/ch02-data/ch2-data.html
9. Papamitsiou, Z., & Economides, A. A. (2022). Learning Analytics and Educational Data Mining in Practice: A Systematic Review of Empirical Evidence. Internet and Higher Education, 55, 100853. https://doi.org/10.1016/j.iheduc.2022.100853
10. Romero, C., Ventura, S., & Pechenizkiy, M. (2024). Educational Data Mining and Learning Analytics: An Updated Survey. Patterns, 5(3), 100663. https://doi.org/10.1016/j.patter.2024.100663
11. Pahl, C. (2004). Data generated by learner interactions with LMS supports learning analytics. Journal of Learning Analytics, 1(2), 94–125.
12. Kovanović, V., Gašević, D., & Siemens, G. (2023). Learning analytics methods and tools. In R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Handbook of Learning Analytics (2nd ed., pp. 21–40). SoLAR.
13. Kimball, R. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley.
14. Integrate.io. (2025). How to build data pipelines for the education industry. Integrate.io Blog.
15. SchoolAnalytix. (2024). Implementing a data warehouse system in education. SchoolAnalytix Blog.
16. Moreira, F., Machado, J., & Costa, C. J. (2022). A dataset for predicting student dropout and academic success. Data, 7(11), 146. https://doi.org/10.3390/data7110146
17. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. https://doi.org/10.1002/widm.1075
18. Sweeney, M., Lester, J., & Rangwala, H. (2016). Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining, 8(1), 22–51.
19. Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Matrix factorization vs. neighborhood models: Reducing the long tail of recommender systems. In Proceedings of the 2010 IEEE International Conference on Computer Science and Education (ICCSE) (pp. 1–5). IEEE.
20. Sarikaya, A., & Correll, M. (2017). Factors affecting visual analytics usage patterns. IEEE Transactions on Visualization and Computer Graphics, 24(1), 286–296. https://doi.org/10.1109/TVCG.2017.2745080
21. Zhang, J., Almeroth, K., Zheng, B., & Niemi, D. (2020). Using learning analytics dashboards to support self-regulated learning in online environments. Educational Technology Research and Development, 68, 1053–1076. https://doi.org/10.1007/s11423-020-09731-1
22. Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
23. Crawley, E. F., Malmqvist, J., Östlund, S., Brodeur, D. R., & Edström, K. (2014). Rethinking engineering education: The CDIO approach (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-05561-9
24. Spady, W. G. (1994). Outcome-Based Education: Critical Issues and Answers. American Association of School Administrators.
25. Daniel, B. K. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
26. Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
27. Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64. https://www.jstor.org/stable/jeductechsoci.17.4.49
28. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
29. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A Reference Model for Learning Analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318–331. https://doi.org/10.1504/IJTEL.2012.051815
30. Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938. https://doi.org/10.1007/s11423-016-9477-y
31. Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167–179. https://doi.org/10.1007/s10676-019-09519-7
32. Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014). Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems. Learning, Media and Technology, 39(1), 10–21. https://doi.org/10.1080/17439884.2013.861517
Related Articles
2025
Iot-Based Power Theft Detector
2025
Comparative Analysis of Conventional and Diagrid Structural Buildings with Plan Irregularity
2025
The Role of C Language in Google, Adobe, and Mozilla Firefox Applications: Performance, Security, and Future Developments
2025
Seismic Analysis of Circular Building and Rectangular Building
2025
Seismic analysis of double-decker elevated water tank
2025