Microlearning Strategies and Knowledge Retention among Undergraduate Students
DOI:
https://doi.org/10.63345/Keywords:
Microlearning, Knowledge Retention, Adaptive Learning, Artificial Intelligence, Spaced Retrieval, Undergraduate EducationAbstract
Microlearning has emerged as a digitally mediated instructional strategy for delivering focused academic content through short, manageable learning units. Despite its growing adoption in higher education, much of the available evidence emphasizes immediate achievement, engagement, or learner satisfaction rather than the durability of knowledge over extended intervals.
This study addresses that limitation by conceptualizing an AI-enabled retention-aware microlearning framework that adapts content sequencing and review intervals according to individual patterns of learning and predicted forgetting. The proposed research examines whether personalized microlearning combined with retrieval practice and adaptive spacing can produce stronger delayed retention than fixed-sequence microlearning and conventional digital instruction among undergraduate students. Interaction traces, assessment histories, response latency, content difficulty, learning frequency, and forgetting indicators are incorporated as learner-state variables for machine-learning-supported personalization. The study further considers cognitive load and disciplinary background as potential sources of heterogeneity in the effectiveness of short-format learning interventions.
References
1. Alias, N., & Abdul Razak, R. (2024). Microlearning in higher education: A systematic review. Journal of Educational Technology & Online Learning.
Useful for: definitions, instructional design, and microlearning in higher education.
2. Leong, K., Sung, A., Au, D., & Blanchard, C. (2021). A review of the trend of microlearning. Journal of Work-Applied Management, 13(1), 88–102. https://doi.org/10.1108/JWAM-10-2020-0044
Useful for: conceptual foundations and development of microlearning.
3. Hug, T. (2005). Micro learning and narration: Exploring possibilities of utilization of narrations and storytelling for the designing of “micro units” and didactical micro-learning arrangements. Proceedings of Media in Transition 4: The Work of Stories.
Useful for: early conceptual foundations of microlearning.
4. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380. https://doi.org/10.1037/0033-2909.132.3.354
Useful for: your theoretical argument concerning spacing and long-term retention. The review synthesized 317 experiments and provides strong evidence for distributed practice.
5. Kerfoot, B. P., DeWolf, W. C., Masser, B. A., Church, P. A., & Federman, D. D. (2007). Spaced education improves the retention of clinical knowledge by medical students: A randomised controlled trial. Medical Education, 41(1), 23–31. https://doi.org/10.1111/j.1365-2929.2006.02644.x
Useful for: your claims concerning spaced learning, retrieval, and delayed knowledge retention. The randomized trial found significantly improved end-of-year retention with spaced education.
6. Microlearning beyond boundaries: A systematic review and a novel framework for improving learning outcomes. (2024). Heliyon. https://doi.org/10.1016/j.heliyon.2024.e41413
Useful for: your core definition of microlearning, instructional design, and learning outcomes. The review synthesized 40 studies using PRISMA.
7. Merino-Campos, C. (2025). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Trends in Higher Education, 4(2), 17. https://doi.org/10.3390/higheredu4020017
Useful for: AI-enabled personalization, adaptive learning, individualized content/pace, and the need for longitudinal research. The review included 45 studies.
8. Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 22, 42. https://doi.org/10.1186/s41239-025-00540-2
Useful for: AI-based educational tools, personalized learning, real-time feedback, and evaluation methodology.
9. Farhood, H., Nyden, M., Beheshti, A., et al. (2025). Artificial intelligence-based personalised learning in education: A systematic literature review. Discover Artificial Intelligence, 5, 331. https://doi.org/10.1007/s44163-025-00598-x
Useful for: adaptive/personalized learning systems and AI algorithms across educational contexts. The review examined 125 studies published between 2015 and June 2025.
