Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis


The emerging need to explore the Web as a learning source allied with the purpose of providing personalized recommendations is a tough task. Considering this scenario, this work presents an approach that combines Semantic Web technologies and bio-inspired algorithms to perform personalized recommendation of Learning Objects (LOs) using local repositories and Web resources. Web resources are retrieved and structured as LOs, which allows for the automatic generation of metadata, minimizing course tutors' work. Experiments were performed to verify which bio-inspired evolutionary algorithm would be most appropriate in this context. Also, discussions regarding the quality of recommendations considering local repositories and Web have been made. Initial experiments evaluating the efficiency of the proposed approach have shown promising results.
Palavras-chave: Personalized recommendation, Learning objects, Bio-inspired algorithms, Ontology


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PEREIRA JÚNIOR, Cleon; BELIZÁRIO JÚNIOR, Clarivando Francisco; ARAÚJO, Rafael D.; DORÇA, Fabiano A.. Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO, 31. , 2020, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 1333-1342. DOI: