Authors:
Sofia Merino Costa
1
;
Rui Pinto
2
and
Gil Gonçalves
2
Affiliations:
1
Departamento Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
;
2
SYSTEC-ARISE, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal
Keyword(s):
Graph-Based Recommendation, Knowledge Management System, Education 5.0, Engineering Education.
Abstract:
The fragmentation of digital learning materials in engineering education makes it difficult for students to find relevant content. This paper presents a graph-based recommender system integrated into an intelligent Knowledge Management System (KMS) to support personalized learning. Using Neo4j, the system models users, learning objects, and semantic relationships to generate contextualized recommendations across dashboard, module, and Learning Path (LP) views. Its scoring mechanism combines semantic similarity, interaction history, and graph proximity to provide adaptive, explainable suggestions. A mixed-methods evaluation with engineering students showed high alignment with user interests and positive perceptions of transparency and personalization. The system effectively transitioned from fallback to tailored recommendations as user interactions increased. Results highlight the potential of graph-based approaches to improve content relevance, discovery, and learner engagement in we
b-based educational platforms, in line with Education 5.0 principles.
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