Adaptive Curriculum Model Based on Self Directed Learning Algorithms for Optimizing Instructional Differentiation in Higher Education
Article Sidebar
Main Article Content
Abstract
The digital transformation of higher education has driven the need for a more adaptive curriculum approach that is responsive to the diverse characteristics of students. Instructional differentiation practices that have been implemented manually and partially have been considered insufficient to address the complexity of heterogeneity in students’ abilities, learning styles, and levels of self-regulation at the institutional scale. This study aimed to formulate and analyze an Adaptive Curriculum Model Based on Self-Directed Learning Algorithms to optimize instructional differentiation in higher education. The study employed a descriptive qualitative approach using a Systematic Literature Review design of scientific publications from 2017 - 2024 obtained from reputable databases. Data were analyzed through content analysis and thematic synthesis to identify conceptual patterns, key components, and empirical trends. The findings indicated that the integration of self directed learning algorithms based on learning data enabled the mapping of student learning profiles, the design of adaptive learning pathways, and the systematic strengthening of self-directed learning. This study produced a conceptual model of an adaptive curriculum integrating learning analytics, digital instructional differentiation, and continuous evaluation within a coherent framework. The contribution of this research lies in reinforcing the perspective of curriculum as a data-driven adaptive system and providing an implementative framework for higher education curriculum development in the era of digital transformation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K–12 and higher education. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
Bond, M., Zawacki-Richter, O., & Nichols, M. (2019). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 50(1), 12–63. https://doi.org/10.1111/bjet.12730
Broadbent, J., & Poon, W. L. (2018). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Gašević, D., Dawson, S., & Siemens, G. (2017). Let’s not forget: Learning analytics are about learning. TechTrends, 61(1), 64–71. https://doi.org/10.1007/s11528-016-0102-5
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
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
Khosravi, H., Kitto, K., & Gašević, D. (2020). Recommender systems for learning: A review and future directions. IEEE Transactions on Learning Technologies, 13(2), 280–295. https://doi.org/10.1109/TLT.2019.2931685
Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2020). Adaptability: Conceptual and empirical perspectives on responses to change, novelty, and uncertainty. Australian Journal of Guidance and Counselling, 22(1), 58–81. https://doi.org/10.1017/jgc.2012.8
OECD. (2021). Digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing. https://doi.org/10.1787/589b283f-en
Phillips, M., McNaught, C., & Kennedy, G. (2019). Evaluating e-learning: Guiding research and practice. Educational Technology & Society, 22(3), 1–15.
Siemens, G., & Baker, R. S. (2017). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the International Conference on Learning Analytics & Knowledge, 252–254.
Tomlinson, C. A., & Moon, T. R. (2017). Assessment and student success in a differentiated classroom. ASCD.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0