Design and Development of Integrations of the Digital Learning Environment to Personalize and Improve the Training Experience of UNITA Students. Use of Learning Analytics Techniques and Data-Driven Decision-Making Strategies to Personalize the Digital Learning Environment

The rapid and large-scale transition to online education triggered by the COVID-19 pandemic has accelerated the adoption of digital learning environments and highlighted the need for flexible, scalable, and learner-centered educational models. In this context, personalization has emerged as a key objective of contemporary digital education, particularly within higher education and lifelong learning scenarios. However, effective personalization requires not only technological solutions, but also robust design models capable of supporting alternative learning pathways while maintaining pedagogical coherence and comparability. This thesis proposes the \textit{SPIRAL} model (Student-centered Personalization of Individual education through Reusable and Autonomous Learning units) as a macro-design framework for online courses and micro-credentials. The model is grounded in the formal structuring of learning outcomes and learning units, enabling the construction of multiple, alternative, and equivalent learning paths leading to the same educational goals. The research integrates natural language processing methods for semantic similarity, and graph-based representations to model relationships among learning outcomes and learning units. These representations enable the analysis of learning paths and support data-driven decision-making in course design and evaluation. The model is implemented and evaluated through applications in different educational contexts, including university courses, competence frameworks, and micro-credential ecosystems. The results demonstrate that the SPIRAL model provides a coherent and extensible approach to designing personalized learning pathways, supporting the identification of alternative routes toward learning outcomes and offering meaningful insights for instructional design and educational planning. The thesis contributes to the field of digital education by bridging instructional design, learning analytics, and personalization, and by providing a design-oriented framework that can inform the development and evaluation of learner-centred digital learning environments. The research also addresses needs that have emerged strongly within the UNITA European Alliance, where the use of digital learning environments plays a central role and the sharing of online learning pathways represents one of the strategic priorities.