• MOURA, Guilherme de Ávila de; SILVA, Williamson; LUNARDI, Gabriel Machado. Em Direção a uma Ferramenta de Recomendação de Código para Apoiar o Aprendizado de Programação. In: LABORATÓRIO DE IDEIAS – SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 3. , 2023, Evento Online. Anais […]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 24-25.
  • Santos, Diego; Gasparini, Isabela and Palazzo Moreira de Oliveira, José. (2023). Recommendation of Educational Resources in a Blended Learning Environment. In Proceedings of the 15th International Conference on Computer Supported Education – Volume 1, ISBN 978-989-758-641-5, ISSN 2184-5026, pages 15-24. CSEDU 2023

Abstract: Blended learning environments are those that combine face-to-face instruction with computer-mediated instruction and have gained space in the means of discussion about new educational methodologies. Several benefits are observed in the use of this methodology, among them: an increase in academic performance and students’ social skills, an increase in teaching and learning flexibility, an increase in student satisfaction, à decrease in dropout rates, and an increase in school retention. Recommender systems are useful in these environments, providing the suggestion of content and activities personalized to users; here, we present a model for recommending learning activities in a blended learning environment. To evaluate the model, SWRL rules were used through the Pellet inference engine. The approach was evaluated through a case study that represents the situation of a student in a blended learning environment, with several options of activities, in which the choices may vary according to their general and academic profiles, in addition to their context. The recommendation rules are executed, resulting in the activity suggestion for the student. Thus, it was verified that the developed model fulfills the proposed objective of enriching the recommendation of learning resources in a blended learning environment through the modeling of the learner’s profile and of the educational resources with context awareness through ontology.

Keywords: Ontologies, Blended Learning, Recommender Systems, Context-aware Systems.


  • Maruyama, Martin H. M.; Silveira, Luan W.; Palazzo M. de Oliveira, José; Gasparini, Isabela and Maran, Vinícius. (2023)Hybrid Recommender System for Educational Resources to the Smart University Campus Domain.  In Proceedings of the 15th International Conference on Computer Supported Education – Volume 1, ISBN 978-989-758-641-5, ISSN 2184-5026, pages 47-56.

Abstract: The development of new cutting-edge technologies in recent years and the ease of access to the internet, the amount of data circulating on the network have been severely increasing, making it difficult to access quality information and causing many users to waste their time looking for and filtering through data. Thus, recommendation systems appears. They are responsible for searching relevant information to the user through mechanisms capable of recognizing the user’s possible interests and, with the use of recommendation algorithms, bringing the user resources that meet their interests. Actually, recommender systems are applied in many domains, including news, healthcare, and finance. Recently, recommender systems have been applied in smart campus domain, which defines systems and technologies to be applied in university campus. From this scenario, the objective of this study is to develop a hybrid recommender system, attached to a software architecture, to provide general educational resources to users. The prototype of the architecture was evaluated using real item data and shown a significant accuracy in the recommendation process.

Keywords: Recommendation Systems, Smart Campus, Collaborative Filtering, Content-Based Filtering