Design-Oriented Personalization in Microcredential Ecosystems: A Graph-Based Analysis of the DEH-EDVANCE

Abstract-Large-scale microcredential initiatives can offer flexible and personalised learning pathways, but their effective implementation requires a coherent, structured organisation at the catalogue level. While research has extensively addressed learner-centric personalisation and adaptive mechanisms, limited attention has been paid to analysing microcredential ecosystems as designed artefacts prior to learner interaction. This paper proposes a design-oriented framework for analysing microcredential catalogues through a graph-based representation of learning outcomes, grounded in the SPIRAL model. Learning outcomes are modelled as nodes and their semantic and cognitive relationships as weighted edges, inferred through a semantic similarity algorithm constrained by Bloom's taxonomy levels. Graph-theoretic metrics, including degree, betweenness centrality, and weighted shortest paths, are used to characterise structural properties such as connectivity, redundancy, and the availability of alternative learning paths. The methodology is applied to a subset of the EDVANCE Digital Education Hub catalogue, developed by the University of Turin. The analysis reveals a structurally compact learning outcome network with heterogeneous connectivity and a non-trivial degree of overlap across courses. Several learning outcomes act as transversal structural bridges, while weighted shortest-path analysis indicates that semantically related out- comes remain connected through relatively short paths, even across course boundaries. The proposed framework provides a descriptive, reproducible basis for analysing the potential for personalisation and the robustness of design in microcredential ecosystems, complementing learner-centric approaches and supporting future design-oriented developments.