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Microlearning and its Effectiveness in Modern Education: A Mini Review

Author(s): Ahmed Mostrady 1 , Eva Sanchez-Lopez 2 , Andres Filipe Gonzalez-Sanchez 3
Author(s) information:
1 School of Education, State University of Zanzibar, Vuga Rd, Zanzibar, Tanzania
2 School of Philosophy and Pedagogy, Universidad Central del Ecuador, Av. Universitaria, Quito 170129, Ecuador
3 Faculty of Education, Universidad de Antioquia, Medellin, Colombia

Corresponding author

The modern educational environment is increasingly moving toward using innovative teaching approaches. One such approach is microlearning, which is characterized by delivering small, focused portions of information that can be quickly consumed and easily retained. Microlearning fits well within the context of digitized education. Its flexibility and adaptability make it a good match for the kinds of short, attention-limited spans that today's learners tend to have. This review will look at the concept of microlearning, the technological platforms that can be used for it, and the effectiveness of microlearning for improving learning outcomes. Several studies have shown that microlearning improves knowledge retention, lowers cognitive load, and allows learners to consume content at their own pace. These studies have established microlearning as a highly effective and flexible modern pedagogical practice. The dissemination of microlearning is now largely in the hands of digital tools—mobile apps, e-learning platforms, and social media—making it more accessible and convenient than ever before. In conclusion, microlearning presents a promising model for modern education, offering substantial cognitive benefits when applied effectively. However, it is essential to balance its use with more in-depth learning strategies to ensure comprehensive understanding.

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Betancur-Chicué, V.; García-Valcárcel Muñoz-Repiso, A. (2023). Microlearning for the Development of Teachers’ Digital Competence Related to Feedback and Decision Making. Education Sciences, 13, 722. https://doi.org/10.3390/educsci13070722.

Ester, P.; Morales, I.; Herrero, L. (2023). Micro-Videos as a Learning Tool for Professional Practice during the Post-COVID Era: An Educational Experience. Sustainability, 15, 5596. https://doi.org/10.3390/su15065596.

Sirwan Mohammed, G.; Wakil, K.; Nawroly, S. (2018). The Effectiveness of Microlearning to Improve Students’ Learning Ability. International Journal of Educational Research Review, 3, 32–38. https://doi.org/10.24331/ijere.415824.

Sun, G.; Cui, T.; Beydoun, G.; Chen, S.; Dong, F.; Xu, D.; Shen, J. (2017). Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem. Sustainability, 9, 898. https://doi.org/10.3390/su9060898.

Nur Fitria, T. (2022). Microlearning in Teaching and Learning Process: A Review. CENDEKIA: Jurnal Ilmu Sosial, Bahasa dan Pendidikan, 2, 114–135. https://doi.org/10.55606/cendikia.v2i4.473.

Skalka, J.; Drlik, M. (2020). Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming Courses. Applied Sciences, 10, 4566. https://doi.org/10.3390/app10134566.

Naveed, Q.N.; Choudhary, H.; Ahmad, N.; Alqahtani, J.; Qahmash, A.I. (2023). Mobile Learning in Higher Education: A Systematic Literature Review. Sustainability, 15, 13566. https://doi.org/10.3390/su151813566.

Dimulescu, C. (2023). E-Learning Platform Usage and Acceptance of Technology after the COVID-19 Pandemic: The Case of Transilvania University. Sustainability, 15, 16120. https://doi.org/10.3390/su152216120.

Criollo-C, S.; Guerrero-Arias, A.; Jaramillo-Alcázar, Á.; Luján-Mora, S. (2021). Mobile Learning Technologies for Education: Benefits and Pending Issues. Applied Sciences, 11, 4111. https://doi.org/10.3390/app11094111.

Song, C.; Shin, S.-Y.; Shin, K.-S. (2023). Optimizing Foreign Language Learning in Virtual Reality: A Comprehensive Theoretical Framework Based on Constructivism and Cognitive Load Theory (VR-CCL). Applied Sciences, 13, 12557. https://doi.org/10.3390/app132312557.

Popovych, V.; Drlik, M. (2024). Identification of Students with Similar Performances in Micro-Learning Programming Courses with Automatically Evaluated Student Assignments. Applied Sciences, 14, 3615. https://doi.org/10.3390/app14093615.

Bitakou, E.; Ntaliani, M.; Demestichas, K.; Costopoulou, C. (2023). Assessing Massive Open Online Courses for Developing Digital Competences among Higher Education Teachers. Education Sciences, 13, 900. https://doi.org/10.3390/educsci13090900.

LinkedIn Learning Comprehensive Review. (2 May 2024) Available online: https://octopuscrm.io/blog/linkedin-learning-review/.

Perifanou, M.; Tzafilkou, K.; Economides, A.A. (2021). The Role of Instagram, Facebook, and YouTube Frequency of Use in University Students’ Digital Skills Components. Education Sciences, 11, 766. https://doi.org/10.3390/educsci11120766.

Abbas, J.; Aman, J.; Nurunnabi, M.; Bano, S. (2019). The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan. Sustainability, 11, 1683. https://doi.org/10.3390/su11061683.

Swanepoel, J.A. (2024). Cohesive Online Education Model Using Emergent Technologies to Improve Accessibility and Impact. Education Sciences, 14, 522. https://doi.org/10.3390/educsci14050522.

Dahri, N.A.; Al-Rahmi, W.M.; Almogren, A.S.; Yahaya, N.; Vighio, M.S.; Al-Maatuok, Q. (2023). Mobile-Based Training and Certification Framework for Teachers’ Professional Development. Sustainability, 15, 5839. https://doi.org/10.3390/su15075839.

Venkat, M.V.; O'Sullivan, P.S.; Young, J.Q.; Sewell, J.L. (2020). Using Cognitive Load Theory to Improve Teaching in the Clinical Workplace. MedEdPORTAL, 16, 10983. https://doi.org/10.15766/mep_2374-8265.10983.

Alias, N.; Razak, R. (2023). Exploring the Pedagogical Aspects of Microlearning in Educational Settings: A Systematic Literature Review. Malaysian Journal of Learning and Instruction, 20. https://doi.org/10.32890/mjli2023.20.2.3.

Balasundaram, S.; Mathew, J.; Nair, S. (2024). Microlearning and Learning Performance in Higher Education: A Post-Test Control Group Study. Journal of Learning for Development, 11(1), 1–14. https://doi.org/10.32890/jld2024.11.1.1.

Ebbinghaus's Forgetting Curve. (accessed on 2 May 2024) Available online: https://www.mindtools.com/a9wjrjw/ebbinghauss-forgetting-curve.

Wollstein, Y.; Jabbour, N. (2022). Spaced Effect Learning and Blunting the Forgetfulness Curve. Ear, Nose & Throat Journal, 101(9_suppl), 42S–46S. https://doi.org/10.1177/01455613231163726.

Pashler, H.; Rohrer, D.; Cepeda, N.J.; et al. (2007). Enhancing learning and retarding forgetting: Choices and consequences. Psychonomic Bulletin & Review, 14, 187–193. https://doi.org/10.3758/BF03194050.

Kang, S.H.K. (2016). Spaced Repetition Promotes Efficient and Effective Learning: Policy Implications for Instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12–19. https://doi.org/10.1177/2372732215624708.

Partarakis, N.; Zabulis, X. (2024). Applying Cognitive Load Theory to eLearning of Crafts. Multimodal Technologies and Interaction, 8, 2. https://doi.org/10.3390/mti8010002.

Namestovski, Ž.; Kovari, A. (2022). Framework for Preparation of Engaging Online Educational Materials—A Cognitive Approach. Applied Sciences, 12, 1745. https://doi.org/10.3390/app12031745.

Fidan, M. (2023). The effects of microlearning-supported flipped classroom on pre-service teachers' learning performance, motivation and engagement. Education and Information Technologies, 28, 1–28. https://doi.org/10.1007/s10639-023-11639-2.

Skalka, J.; Drlik, M.; Benko, L.; Kapusta, J.; Rodríguez del Pino, J.C.; Smyrnova-Trybulska, E.; Stolinska, A.; Svec, P.; Turcinek, P. (2021). Conceptual Framework for Programming Skills Development Based on Microlearning and Automated Source Code Evaluation in Virtual Learning Environment. Sustainability, 13, 3293. https://doi.org/10.3390/su13063293.

Sozio, G.; Agostinho, S.; Tindall-Ford, S.; Paas, F. (2024). Enhancing Teaching Strategies through Cognitive Load Theory: Process vs. Product Worked Examples. Education Sciences, 14, 813. https://doi.org/10.3390/educsci14080813.

Crandall, R.; Karadoğan, E. (2021). Designing Pedagogically Effective Haptic Systems for Learning: A Review. Applied Sciences, 11, 6245. https://doi.org/10.3390/app11146245.

Gligorea, I.; Cioca, M.; Oancea, R.; Gorski, A.-T.; Gorski, H.; Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13, 1216. https://doi.org/10.3390/educsci13121216.

Sivagurunathan, S.; Parthasarathy, S. (2022). A Framework for a Seamless Transformation to Online Education. Computers, 11, 183. https://doi.org/10.3390/computers11120183.

About this article

SUBMITTED: 16 September 2024
ACCEPTED: 06 November 2024
PUBLISHED: 8 November 2024
SUBMITTED to ACCEPTED: 52 days
DOI: https://doi.org/10.53623/apga.v4i1.496

Cite this article
Mostrady, A., Sanchez-Lopez, E. ., & Gonzalez-Sanchez, A. F. . (2024). Microlearning and its Effectiveness in Modern Education: A Mini Review. Acta Pedagogia Asiana, 4(1), 33–42. https://doi.org/10.53623/apga.v4i1.496
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