Adaptive Micro learning and Its Effect on Secondary-School Students’ Achievement A Quasi-Experimental Study in a Computer Science Course at Rabia Al-Adawiya Secondary School, Msallata, Libya
DOI:
https://doi.org/10.65405/hkzmpg12Keywords:
microlearning; adaptive learning; concept maps; secondary education; computer science; academic achievement.Abstract
This study examined the effect of an adaptive microlearning system on the achievement of second-year secondary-school students (science track) in a computer science course. A Moodle plugin was developed in PHP that divides the course content into ten microlearning units linked by a concept map. After an initial test, each student is classified into one of three categories (fail, pass with a need for remedial reinforcement, pass) and is routed to prerequisite materials or targeted recommendations, followed by a follow-up assessment. A quasi-experimental design with historical control cohorts was adopted. A random sample of 64 students was drawn from each of four cohorts (2022–2023 to 2025–2026; N = 256). The system was applied to the 2025–2026 cohort for a full academic year, whereas the three earlier cohorts were taught traditionally. A one-way ANOVA revealed significant differences among the cohorts, F(3, 252) = 13.52, p < .001, η² = 0.14. The 2025–2026 cohort obtained a mean score of 75.25 versus 68.45 for the earlier cohorts combined (difference = 6.80 points; Cohen’s d = 0.91), and the proportion of students scoring 70 or above rose from 45.8% to 81.3%. Usage indicators (units completed and number of attempts) were not significantly related to final grades. The findings indicate that adopting the system was associated with substantially higher achievement; however, because the historical-control design is not randomised, causal claims should be made cautiously. Directions for randomised, pre-test-controlled follow-up studies are outlined.
Downloads
References
[1] A. Ezzaim, A. Dahbi, A. Haidine, and A. Aqqal, “The impact of implementing a Moodle plug-in as an AI-based adaptive learning solution on learning effectiveness: Case of Morocco,” International Journal of Interactive Mobile Technologies (iJIM), vol. 18, no. 1, pp. 133–149, 2024, doi: 10.3991/ijim.v18i01.46309.
[2] T. Kabudi, I. Pappas, and D. H. Olsen, “AI-enabled adaptive learning systems: A systematic mapping of the literature,” Computers and Education: Artificial Intelligence, vol. 2, Art. no. 100017, 2021, doi: 10.1016/j.caeai.2021.100017.
[3] N. Kara and N. Sevim, “Adaptive learning systems: Beyond teaching machines,” Contemporary Educational Technology, vol. 4, no. 2, pp. 108–120, 2013, doi: 10.30935/cedtech/6095.
[4] T. Hug, “Micro learning and narration: Exploring possibilities of utilization of narrations and storytelling for the designing of ‘micro units’ and didactical micro learning arrangements,” presented at the Fourth Media in Transition Conf., MIT, Cambridge, MA, USA, May 2005.
[5] T. Hug and N. Friesen, “Outline of a microlearning agenda,” in Didactics of Microlearning: Concepts, Discourses and Examples, T. Hug, Ed. Münster, Germany: Waxmann, 2007, pp. 15–31.
[6] I. Buchem and H. Hamelmann, “Microlearning: A strategy for ongoing professional development,” eLearning Papers, no. 21, pp. 1–15, 2010.
[7] M. J. Dolasinski and J. Reynolds, “Microlearning: A new learning model,” Journal of Hospitality & Tourism Research, vol. 44, no. 3, pp. 551–561, 2020.
[8] J. C. De Gagne, H. K. Park, K. Hall, A. Woodward, S. Yamane, and S. S. Kim, “Microlearning in health professions education: Scoping review,” JMIR Medical Education, vol. 5, no. 2, Art. no. e13997, 2019.
[9] C. Wang, M. Bakhet, D. Roberts, S. Gnani, and A. El-Osta, “The efficacy of microlearning in improving self-care capability: A systematic review of the literature,” Public Health, vol. 186, pp. 286–296, 2020.
[10] G. M. Zambrano Zambrano and P. A. Toala Medina, “Análisis del efecto del microaprendizaje en el desempeño académico de los estudiantes de la Unidad Educativa ‘Edwin Mendoza’,” Mendive. Revista de Educación, vol. 23, no. 2, Art. no. e4029, 2025.
[11] M. Attia and M. Arteimi, “Adaptive e-learning system using fuzzy logic,” M.S. thesis, Libyan Academy for Postgraduate Studies, Libya, 2021.
[12] K. Leong, A. Sung, D. Au, and C. Blanchard, “A review of the trend of microlearning,” Journal of Work-Applied Management, vol. 13, no. 1, pp. 88–102, 2021.
[13] H. A. El-Sabagh, “Adaptive e-learning environment based on learning styles and its impact on development students’ engagement,” International Journal of Educational Technology in Higher Education, vol. 18, Art. no. 53, 2021, doi: 10.1186/s41239-021-00289-4.
[14] I. Karagiannis and M. Satratzemi, “An adaptive mechanism for Moodle based on automatic detection of learning styles,” Education and Information Technologies, vol. 23, no. 3, pp. 1331–1357, 2018, doi: 10.1007/s10639-017-9663-5.
[15] G. Sun, T. Cui, G. Beydoun, S. Chen, F. Dong, D. Xu, and J. Shen, “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, vol. 9, no. 6, Art. no. 898, 2017, doi: 10.3390/su9060898.
[16] J. Lin, G. Sun, J. Shen, T. Cui, P. Yu, D. Xu, et al., “A survey of segmentation annotation and recommendation techniques in micro learning for next generation of OER,” in Proc. IEEE 23rd Int. Conf. Computer Supported Cooperative Work in Design (CSCWD), 2019, pp. 152–157.
[17] W. Hamidi, S. Ziti, and Y. Z. Seghroucheni, “AI-powered adaptive microlearning: The logical evolution of online learning systems,” in Digital Technologies and Applications (Lecture Notes in Networks and Systems, vol. 1641), S. Motahhir, B. Bossoufi, and J. M. Guerrero, Eds. Cham, Switzerland: Springer, 2026, doi: 10.1007/978-3-032-06061-7_21.
[18] R. Salhab and M. M. Aboushi, “Impact of AI-assisted microlearning on student engagement in an online environment in higher education,” Frontiers in Education, vol. 11, Art. no. 1766032, 2026, doi: 10.3389/feduc.2026.1766032.
[19] J. D. Novak, “Concept mapping: A useful tool for science education,” Journal of Research in Science Teaching, vol. 27, no. 10, pp. 937–949, 1990, doi: 10.1002/tea.3660271003.
[20] J. D. Novak and A. J. Cañas, “The theory underlying concept maps and how to construct and use them,” Florida Inst. for Human and Machine Cognition, Pensacola, FL, USA, Tech. Rep. IHMC CmapTools 2006-01 Rev 01-2008, 2008.
[21] S.-S. Tseng, P.-C. Sue, J.-M. Su, J.-F. Weng, and W.-N. Tsai, “A new approach for constructing the concept map,” Computers & Education, vol. 49, no. 3, pp. 691–707, 2007.
[22] Y. Pang, N. Wang, Y. Zhang, et al., “Prerequisite-related MOOC recommendation on learning path locating,” Computational Social Networks, vol. 6, no. 1, pp. 1–16, 2019, doi: 10.1186/s40649-019-0065-2.
[23] A. Chanaa and N.-E. El Faddouli, “Prerequisites-based course recommendation: Recommending learning objects using concept prerequisites and metadata matching,” Smart Learning Environments, vol. 11, Art. no. 16, 2024, doi: 10.1186/s40561-024-00301-0.
[24] R. Manrique, B. Pereira, and O. Mariño, “Exploring knowledge graphs for the identification of concept prerequisites,” Smart Learning Environments, vol. 6, no. 1, pp. 1–18, 2019, doi: 10.1186/s40561-019-0104-3.
[25] T. Krahn, R. Kuo, and M. Chang, “Personalized study guide: A Moodle plug-in generating personal learning path for students,” in Augmented Intelligence and Intelligent Tutoring Systems (ITS 2023), LNCS vol. 13891, C. Frasson, P. Mylonas, and C. Troussas, Eds. Cham, Switzerland: Springer, 2023, pp. 333–341, doi: 10.1007/978-3-031-32883-1_30.
[26] A. Zabolotskikh, A. Zabolotskikh, T. Dugina, and D. Tavberidze, “Creating individual learning paths in the Moodle plugin for undergraduate students to study English grammar,” Education and Information Technologies, vol. 26, no. 1, pp. 617–637, 2021, doi: 10.1007/s10639-020-10278-1.
[27] J. Sweller, “Cognitive load during problem solving: Effects on learning,” Cognitive Science, vol. 12, no. 2, pp. 257–285, 1988.
[28] R. E. Mayer, Multimedia Learning, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 2009.
[29] B. S. Bloom, “Learning for mastery,” Evaluation Comment, vol. 1, no. 2, pp. 1–12, 1968.
[30] W. R. Shadish, T. D. Cook, and D. T. Campbell, Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA, USA: Houghton Mifflin, 2002.
[31] M. Delacre, D. Lakens, and C. Leys, “Why psychologists should by default use Welch’s t-test instead of Student’s t-test,” International Review of Social Psychology, vol. 30, no. 1, pp. 92–101, 2017.
[32] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1988.
[33] ALNNALE, T. (2026). Teaching Methods and Methods and Their Impact on the Performance of Faculty Members Field Study. Shihab Journal of Humanities, 2(2), 53-79.












