Adaptive Learning Technologies and Student Achievement in Higher Education
DOI:
https://doi.org/10.63345/Keywords:
Adaptive learning technologies, artificial intelligence in education, student achievement, personalized learning, learning analytics, higher educationAbstract
Adaptive learning technologies are increasingly being integrated into higher education to personalize instructional content, feedback, difficulty, and learning sequences using learner-generated data.
Despite growing evidence of positive academic outcomes, uncertainty remains regarding whether achievement gains arise from algorithmic adaptation itself or from greater practice opportunities, feedback frequency, and time spent interacting with digital platforms.
This study addresses this adaptation-attribution gap by conceptualizing student achievement as an outcome of dynamically matched instructional decisions rather than technology exposure alone. A learning-analytics framework is proposed in which evolving learner knowledge, response accuracy, learning velocity, and behavioral engagement are used to estimate when and how instructional adaptation should occur.
The study distinguishes adaptive decision quality from conventional measures of platform usage and examines whether appropriate content sequencing contributes to achievement beyond prior academic ability and learning intensity. The proposed research further considers heterogeneity in adaptive benefits, recognizing that students with different initial knowledge states may respond differently to identical personalization strategies.
References
1. Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, 100489. https://doi.org/10.1016/j.edurev.2022.100489
2. du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
3. Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103
4. Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4, 170171. https://doi.org/10.1038/sdata.2017.171
5. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
6. Zhong, L. (2023). A systematic review of personalized learning in higher education: Learning content structure, learning materials sequence, and learning readiness support. Interactive Learning Environments, 31(10), 7053–7073. https://doi.org/10.1080/10494820.2022.2061006
7. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
8. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer.
9. Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.
10. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
