Smart leveling: an AI-driven adaptive learning strategy in higher education
DOI:
https://doi.org/10.5944/ried.45482Keywords:
adaptive learning, artificial intelligence, academic leveling, higher educationAbstract
Academic leveling is one of the main challenges in higher education, particularly in STEM disciplines—a challenge that has intensified following academic lockdowns and for which traditional remedial courses have not yielded the expected results. This study evaluated the impact of an adaptive learning strategy (ALS) designed to reinforce prior knowledge in 1,309 first-year students enrolled in the courses of Computational Thinking, Fundamental Mathematical Modeling, Mathematical Reasoning, and Mathematical Thinking at a private university in Mexico. Unlike conventional remedial approaches, the ALS offers a flexible, student-centered learning experience through brief adaptive modules integrated into regular courses. These modules, supported by an artificial intelligence platform, generate personalized learning pathways, adapt content, produce analytics, and facilitate data-driven instructional decision-making. The research adopted a mixed-methods approach (QUAN > QUAL) and a quasi-experimental design with matched samples to ensure group comparability. Results revealed statistically significant differences in academic performance in favor of the experimental groups compared to the control groups. Additionally, both students and instructors evaluated the ALS positively in terms of usefulness and learning experience. Findings suggest that an adaptive, integrated, and technology-mediated strategy is a promising alternative to address academic leveling in introductory university courses, offering substantial advantages over traditional remedial methods.
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Allela, M. A., Ogange, B. O., Junaid, M. I., & Charles, P. B. (2020). Effectiveness of multimodal microlearning for in-service teacher training. Journal of Learning for Development, 7(3), 384-398. https://doi.org/10.56059/jl4d.v7i3.387
Cavanagh, T., Chen, B., Ait Maalem Lahcen, R., & Paradiso, J. R. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. The International Review of Research in Open and Distributed Learning, 21(1), 173-195. https://www.irrodl.org/index.php/irrodl/article/view/4557
Creswell, J. W. (2015). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (5th ed.). Pearson.
Dagunduro, A. O., Chikwe, C. F., Ajuwon, O. A., & Ediae, A. A. (2024). Adaptive learning models for diverse classrooms: Enhancing educational equity. International Journal of Applied Research in Social Sciences, 6(9), 2228-2240. https://doi.org/10.51594/ijarss.v6i9.1588
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
Dubey, A., Chen, B., & Lloyd, J. (2023, 4 May). Personalized adaptive learning in algebra-based introductory physics courses at the University of Central Florida. WCET Frontiers. https://wcet.wiche.edu/frontiers/2023/05/04/personalized-adaptive-learning-in-algebra-based-intro-physics-courses-at-ucf/
Eau, G., Hoodin, D., & Musaddiq, T. (2022). Testing the effects of adaptive learning courseware on student performance: An experimental approach. Southern Economic Journal, 88(3), 1086-1118. https://doi.org/10.1002/soej.12547
Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57-72. https://doi.org/10.1002/j.2168-9830.2005.tb00829.x
Fernández-Morante, C., Cebreiro-López, B., Rodríguez-Malmierca, M.-J., & Casal-Otero, L. (2022). Adaptive learning supported by learning analytics for student teachers’ personalized training during in-school practices. Sustainability, 14(1), 124. https://doi.org/10.3390/su14010124
Filatova, E., Chen, Y.-W., & Li, H. (2023, June). Analysis of the COVID-19 impact on students’ enrollment, performance, and retention. In Proceedings of the 2023 ASEE Annual Conference & Exposition (Baltimore, MD, United States). https://doi.org/10.18260/1-2--42660
González-Beltrán, B. A., González-Brambila, S. B., Sánchez-Guerrero, L., Ardón-Pulido, I., & Figueroa-González, J. (2018). Estudio del impacto de un curso de nivelación en el desempeño de alumnos de ingeniería utilizando minería de datos educacional. Research in Computing Science, 147(5), 159-172. https://doi.org/10.13053/rcs-147-5-12
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. https://doi.org/10.4324/9780203887332
Hattie, J. (2017). Visible Learning Plus: 252 influences and effect sizes related to student achievement [PDF]. Visible Learning. https://visible-learning.org/wp-content/uploads/2018/03/VLPLUS-252-Influences-Hattie-ranking-DEC-2017.pdf
Hernandez Cardenas, L. S., Castano, L., Cruz Guzman, C., & Nigenda Alvarez, J. P. (2022). Personalised learning model for academic leveling and improvement in higher education. Australasian Journal of Educational Technology, 38(2), 70-82. https://doi.org/10.14742/ajet.7084
Howlin, C., & Lynch, D. (2014, October). Learning and academic analytics in the Realizeit system. In Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (E-Learn 2014) (New Orleans, LA, United States). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/148952/
Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research landscape of adaptive learning in education: A bibliometric study on research publications from 2000 to 2022. Sustainability, 15(4), 3115. https://doi.org/10.3390/su15043115
Kara, N., & Sevim, N. (2013). Adaptive learning systems: Beyond teaching machines. Contemporary Educational Technology, 4(2), 108-120. https://doi.org/10.30935/cedtech/6095
Klingbeil, N. W., Rattan, K. S., Raymer, M. L., Reynolds, D. B., Mercer, R., Kukreti, A., & Randolph, B. (2007, June). A national model for engineering mathematics education. In Proceedings of the 2007 ASEE Annual Conference & Exposition (Honolulu, HI, United States). https://doi.org/10.18260/1-2--2104
Koester, B. P., Grom, G., & McKay, T. A. (2016). Patterns of gendered performance difference in introductory STEM courses [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1608.07565
Lasater, K., Smith, C., Pijanowski, J., & Brady, K. P. (2021). Redefining mentorship in an era of crisis: Responding to COVID-19 through compassionate relationships. International Journal of Mentoring and Coaching in Education, 10(2), 158-172. https://doi.org/10.1108/IJMCE-11-2020-0078
Li, T., Kirk, C., & Oseguera, L. (2023). Heterogeneous academic achievement profiles of initially STEM-intending students over the college years. Journal of College Student Development, 64(6), 728-735. https://doi.org/10.1353/csd.2023.a917027
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31-40. https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education
López del Puerto, C., Bellido, C. M., Suárez, O. M., Alfaro, M., & Jiménez, M. A. (2021, July). Championing Hispanic student success following natural disasters in Puerto Rico. In Proceedings of the 2021 ASEE Annual Conference & Exposition (evento virtual). https://doi.org/10.18260/1-2--36790
McKenna, A., Agogino, A. M., & McMartin, F. (2000, October). What students say about learning physics, math, and engineering. In Proceedings of the 30th Annual Frontiers in Education Conference (Kansas City, MO, United States). IEEE. https://doi.org/10.1109/FIE.2000.897580
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). Inteligencia artificial y educación: Guía para las personas a cargo de formular políticas. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000379376
Moskal, P., Carter, D., & Johnson, D. (2017, 4 January). 7 things you should know about... adaptive learning. EDUCAUSE Learning Initiative. https://library.educause.edu/resources/2017/1/7-things-you-should-know-about-adaptive-learning
Ochukut, S. A., Oboko, R. O., Miriti, E., & Maina, E. (2023). Research trends in adaptive online learning: Systematic literature review (2011–2020). Technology, Knowledge and Learning, 28, 431-448. https://doi.org/10.1007/s10758-022-09615-9
Olmos-López, O., Rincon-Flores, E. G., Mena, J., Román, O., & Camacho-López, E. (2023). Artificial intelligence as a way to improve educational practices. In M. Cebral-Loureda, E. G. Rincon-Flores, & G. Sanchez-Ante (Eds.), What AI can do: Strengths and limitations of artificial intelligence (pp. 135-151). Taylor & Francis. https://doi.org/10.1201/b23345-10
Press Trust of India. (2022, 3 May). Haryana govt to distribute over 5 lakh free tablets to Class 10, 12 students. The Indian Express. https://indianexpress.com/article/education/haryana-govt-to-distribute-over-5-lakh-free-tablets-to-class-10-12-students-from-may-5-7898959/
Rincon-Flores, E. G., Castano Sánchez, L., Guerrero Solís, S. L., Olmos López, O., Rodríguez Hernández, C. F., Castillo Lara, L. A., & Aldape Valdes, L. P. (2024). Improving the learning-teaching process through adaptive learning strategy. Smart Learning Environments, 11, 27. https://doi.org/10.1186/s40561-024-00314-9
Rincon-Flores, E. G., Rivera Vázquez, N., Castano Sánchez, L., Guerrero Solís, S. L., Aldape Valdes, L. P., & Castillo Lara, L. A. (2023, April). Improving face-to-face learning using an adaptive technology. In Proceedings of the 2023 IEEE Global Engineering Education Conference (EDUCON) (Kuwait City, Kuwait). IEEE. https://doi.org/10.1109/EDUCON54358.2023.10125276
Rockinson-Szapkiw, A., Herring Watson, J., Gishbaugher, J., & Wendt, J. L. (2021). A case for a virtual STEM peer-mentoring experience for racial and ethnic minority women mentees. International Journal of Mentoring and Coaching in Education, 10(3), 267-283. https://doi.org/10.1108/IJMCE-08-2020-0053
Ross, P. M., Scanes, E., Poronnik, P., Coates, H., & Locke, W. (2022). Understanding STEM academics’ responses and resilience to educational reform of academic roles in higher education. International Journal of STEM Education, 9(1), 11. https://doi.org/10.1186/s40594-022-00327-1
Sarıyalçınkaya, A. D., Karal, H., Altinay, F., & Altinay, Z. (2021). Reflections on adaptive learning analytics: Adaptive learning analytics. In A. Azevedo, J. M. Azevedo, J. O. Uhomoibhi, & E. Ossiannilsson (Eds.), Advancing the power of learning analytics and big data in education (pp. 61-84). IGI Global. https://doi.org/10.4018/978-1-7998-7103-3.ch003
Schaal, N., Chan, S. E., & Saint Clair, J. K. (2021, July). Insights gleaned from the GAIN peer-mentoring program pilot. In Proceedings of the 2021 ASEE Annual Conference & Exposition (evento virtual). https://peer.asee.org/insights-gleaned-from-the-gain-peer-mentoring-program-pilot
Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: why undergraduates leave the sciences. Westview Press.
Shi, P., & Liu, W. (2025). Adaptive learning oriented higher educational classroom teaching strategies. Scientific Reports, 15, 15661. https://doi.org/10.1038/s41598-025-00536-y
Simon, P. D., & Zeng, L. M. (2024). Behind the scenes of adaptive learning: A scoping review of teachers’ perspectives on the use of adaptive learning technologies. Education Sciences, 14(12), 1413. https://doi.org/10.3390/educsci14121413
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296. https://doi.org/10.1023/A:1022193728205
Taha, H., Hanim, F., Johar, M., & Shah, M. (2015). Peer mentoring module: The effect of an intervention of academic mentoring program towards motivation and self-esteem among foundation students in Malaysia. Universal Journal of Psychology, 3(3), 80-83. https://doi.org/10.13189/ujp.2015.030304
Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021). Personalized and adaptive learning. In J. Ryoo & K. Winkelmann (Eds.), Innovative learning environments in STEM higher education: Opportunities, challenges, and looking forward (pp. 17-34). Springer. https://doi.org/10.1007/978-3-030-58948-6_2
UNESCO. (2019). Consenso de Beijing sobre la inteligencia artificial y la educación [Documento de programa]. https://unesdoc.unesco.org/ark:/48223/pf0000368303
Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes. Harvard University Press.
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17. https://doi.org/10.1207/s15326985ep2501_2
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