From Learning Management Systems to Adaptive Learning Systems: A Systematic Architectural Transformation Framework
Resumo
Traditional Learning Management Systems (LMS) predominantly operate as static content repositories under a ”one-size-fits-all” paradigm, failing to address the diverse cognitive needs and learning paces of individual students. While Adaptive Learning Systems (ALS) offer a solution through personalization, migrating legacy monolithic LMS infrastructures involves overcoming significant technical debt and ”architectural stiffness.” This paper proposes ARALS (Adaptive Reference Architecture for Learning Systems), a framework designed to evolve legacy LMSs into genuine adaptive environments without requiring a complete codebase rewrite. To this end, we introduce a Conceptual Transformation Sequence (C1) and the PALS framework (Properties of Adaptive Learning Systems) as a systematic method to inject and validate adaptivity. ARALS integrates the MAPE-K autonomic computing cycle into a decoupled, seven/eight-layer structure, acting as a control mechanism for the operational flow to ensure pedagogical adaptation. The proposal is validated through a high-fidelity case study in which the Odoo eLearning module is architecturally refactored. Results demonstrate the successful implementation of dynamic ”Learning Roadmaps” and personalized content sequencing with negligible performance overhead, proving the viability of ARALS as an interdisciplinary bridge between software engineering and adaptive pedagogy.Referências
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Raj, N. S. and Renumol, V. G. (2024). An improved adaptive learning path recommendation model driven by real-time learning analytics. Journal of Computers in Education, 11(1):121–148.
Senthil, S., Ramesh, T., and Aswini, K. (2024). An adaptive optimization algorithm for personalized learning pathways in e-learning. International Journal of Business and Engineering Systems (IJB&ES), 9(2):28–36.
Shakya, A. K., Pillai, G., and Chakrabarty, S. (2023). Reinforcement learning algorithms: A brief survey. Expert Systems with Applications, 231:120495.
Ubaydullaeva, S., Umurova, G., Botirova, S., Yakhshiev, A., Mavlyanova, U., Nazirova, S., and Kim, O. (2024). Modular web-based learning model to address underdeveloped ict infrastructure for smart e-learning education system. Journal of Internet Services and Information Security (JISIS), 14(4):450–461.
Vummannagari, S. (2025). Intelligent system evolution: The ai-enhanced strangler pattern transforming legacy architecture. Journal Of Engineering And Computer Sciences, 4(7):1–11. Valida la estrategia de evolución de ARALS mediante la refactorización no intrusiva de sistemas legados.
Aljaloud, A. and Razzaq, A. (2023). An innovative metric-based clustering approach for increased scalability and dependency elimination in monolithic legacy systems. Engineering, Technology & Applied Science Research, 13(4):11177–11184.
Bass, L., Clements, P., and Kazman, R. (1997). Software Architecture in Practice. Addison-Wesley Professional, 1st edition.
Berglund, A. (2024). Development of a fully functioning artificial design tutor–a quest for reframing intelligent tutoring systems. In Proceedings of the International Conference on Engineering and Product Design Education (E&PDE 2024), DS 131, pages 581–586.
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6):4–16.
Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. UMUAI.
El Hadbi, A., Rziki, M. H., Jamil, Y., Ammari, Z., Khalifa, B. M., Bourray, H., and El Ouadghiri, D. (2025). Design and implementation of an adaptive tutoring system for enhanced e-learning. Data & Metadata, 4:469.
Escober, A. G. E. and Monzon, D. E. (2025). Optimizing engagement and retention through data-driven personalization. IJLTEMAS.
Farshidi, S., Jansen, S., and van der Werf, J. M. (2020). Capturing software architecture knowledge for pattern-driven design. Journal of Systems and Software, 169:110714.
Goldin, T., Rauch, E., Pacher, C., and Woschank, M. (2022). Reference architecture for an integrated and synergetic use of digital tools in education 4.0. Procedia Computer Science, 200:407–417. 3rd International Conference on Industry 4.0 and Smart Manufacturing.
Halima, R. B., Hachicha, M., Jemal, A., and Kacem, A. H. (2023). Mape-k patterns for self-adaptation in cyber-physical systems. The Journal of Supercomputing, 79(5):4917–4943.
Hassan, H., Abdel-Fattah, M. A., and Mohamed, W. S. (2024). Migrating from monolithic to microservice architectures: A systematic literature review. International Journal of Advanced Computer Science and Applications (IJACSA), 15(10).
Ibrahim, A., Bolaji, H. O., and Abdulraheem, A. J. (2025). Accessibility and utilization of artificial intelligence (ai)-based intelligent tutoring systems (its) and information and communication technology (ict) in enhancing biology education. ASEAN Journal for Science Education, 4(2):93–104.
Ibrahim, A. H., Eliemy, M., and Youssif, A. A. (2023). An enhanced adaptive learning system based on microservice architecture. Future Computing and Informatics Journal, 8(1):Article 4.
Jung, H. S., Lee, H., and Park, K. C. (2025). Analysis of ux elements in educational applications for young children and implementation of iso/iec 25010 quality standards. SAGE Open, 15(3).
Kabudi, T., Pappas, I., and Olsen, D. H. (2021). Ai-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2:100017.
Koch, N. and Wirsing, M. (2000). The munich reference model for adaptive hypermedia applications. In Adaptive Hypermedia and Adaptive Web-Based Systems, volume 1892 of Lecture Notes in Computer Science, pages 213–222. Springer.
Kővári, A. (2025). AI Gem: Context-Aware Transformer Agents as Digital Twin Tutors for Adaptive Learning. Computers, 14(9):367.
Kucharski, S., Braun, I., and Kubica, T. (2023). An adaptive, structure-aware intelligent tutoring system for learning management systems. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), pages 367–369. IEEE.
Liu, V., Latif, E., and Zhai, X. (2025). Advancing education through tutoring systems: A systematic literature review. arXiv preprint arXiv:2503.09748.
Martin, F., Chen, Y., Moore, R. L., and Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4):1903–1929.
Me, L., Devi, S., Shuba, S., Sneha, K., and Sofiya, S. (2024). Adaptive learning management system. In Proceedings of the International Conference on Recent Challenges in Computing and Technology (ICRCCT 2024).
Monsalve-Pulido, J., Aguilar, J., and Montoya, E. (2023). Framework for the adaptation of an autonomous academic recommendation system as a service-oriented architecture. Education and Information Technologies, 28(1):321–341.
Nepomuceno, A. R., Domínguez, E. L., Isidro, S. D., Nieto, M. A. M., Meneses-Viveros, A., and de la Calleja, J. (2024). Software architectures for adaptive mobile learning systems: A systematic literature review. Applied Sciences, 14(11):4540.
Raj, N. S. and Renumol, V. G. (2024). An improved adaptive learning path recommendation model driven by real-time learning analytics. Journal of Computers in Education, 11(1):121–148.
Senthil, S., Ramesh, T., and Aswini, K. (2024). An adaptive optimization algorithm for personalized learning pathways in e-learning. International Journal of Business and Engineering Systems (IJB&ES), 9(2):28–36.
Shakya, A. K., Pillai, G., and Chakrabarty, S. (2023). Reinforcement learning algorithms: A brief survey. Expert Systems with Applications, 231:120495.
Ubaydullaeva, S., Umurova, G., Botirova, S., Yakhshiev, A., Mavlyanova, U., Nazirova, S., and Kim, O. (2024). Modular web-based learning model to address underdeveloped ict infrastructure for smart e-learning education system. Journal of Internet Services and Information Security (JISIS), 14(4):450–461.
Vummannagari, S. (2025). Intelligent system evolution: The ai-enhanced strangler pattern transforming legacy architecture. Journal Of Engineering And Computer Sciences, 4(7):1–11. Valida la estrategia de evolución de ARALS mediante la refactorización no intrusiva de sistemas legados.
Publicado
11/05/2026
Como Citar
SARTORIO, Alejandro; ROSSI, Gustavo.
From Learning Management Systems to Adaptive Learning Systems: A Systematic Architectural Transformation Framework. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 29. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
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p. 430-444.
