Please use this identifier to cite or link to this item: https://repositorio.ufu.br/handle/123456789/43251
ORCID:  http://orcid.org/0000-0003-1873-3643
Document type: Tese
Access type: Acesso Aberto
Title: An approach to the personalized learning objects recommendation problem as a set covering problem using ontologies and metaheuristics
Alternate title (s): Uma abordagem para o problema de recomendação personalizada de objetos de aprendizagem como um problema de cobertura de conjuntos usando ontologias e metaheurísticas
Author: Belizário Júnior, Clarivando Francisco
First Advisor: Dorça, Fabiano Azevedo
First coorientator: Assis, Luciana Pereira de
Second coorientator: Andrade, Alessandro Vivas
First member of the Committee: Fernandes, Márcia Aparecida
Second member of the Committee: Cattelan, Renan Gonçalves
Third member of the Committee: Oliveira, José Palazzo Moreira de
Fourth member of the Committee: Costa, Evandro de Barros
Summary: Recommender Systems are extensively utilized in e-commerce platforms, such as sales websites and Netflix, to intelligently suggest products, movies, and series tailored to the user’s preferences. In the context of education, the key challenge for these systems is to provide personalized recommendations of educational content that align with students’ needs, considering their knowledge levels, learning styles, and cognitive preferences. This work implements a recommender system designed to suggest learning objects across various areas of knowledge, integrating small learning objects, called interventions, such as definitions, examples, and hints. To personalize these recommendations, the Learning Objects Recommendation Problem is formulated as a set-covering problem, which belongs to the class of NP-Hard problems. A heuristic search-based algorithm was proposed and compared with other metaheuristics, resulting in a promising approach to solving this problem, as demonstrated by the results. The proposed solution aims to minimize the challenges of cold-start and rating sparsity, common in traditional recommender systems, by using advanced collaborative filtering techniques and an ontology that models the students’ needs, knowledge, learning styles, and search parameters. Additionally, the recommender system was implemented with a chatbot and tested for recommending content on the C programming language for first-year students of the Computer Science course, using gamification to alleviate possible pedagogical difficulties in the teaching-learning process.
Abstract: Recommender Systems are extensively utilized in e-commerce platforms, such as sales websites and Netflix, to intelligently suggest products, movies, and series tailored to the user’s preferences. In the context of education, the key challenge for these systems is to provide personalized recommendations of educational content that align with students’ needs, considering their knowledge levels, learning styles, and cognitive preferences. This work implements a recommender system designed to suggest learning objects across various areas of knowledge, integrating small learning objects, called interventions, such as definitions, examples, and hints. To personalize these recommendations, the Learning Objects Recommendation Problem is formulated as a set-covering problem, which belongs to the class of NP-Hard problems. A heuristic search-based algorithm was proposed and compared with other metaheuristics, resulting in a promising approach to solving this problem, as demonstrated by the results. The proposed solution aims to minimize the challenges of cold-start and rating sparsity, common in traditional recommender systems, by using advanced collaborative filtering techniques and an ontology that models the students’ needs, knowledge, learning styles, and search parameters. Additionally, the recommender system was implemented with a chatbot and tested for recommending content on the C programming language for first-year students of the Computer Science course, using gamification to alleviate possible pedagogical difficulties in the teaching-learning process.
Keywords: Learning objects recommendation
Collaborative filtering
Learning styles
Ontology
Set covering
Recommender system
Chatbot
Gamification
Area (s) of CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
Subject: Ciência da Computação
Inteligência artificial
Inteligência artificial - Aplicações educacionais
Ontologias (Recuperação da informação)
Language: eng
Country: Brasil
Publisher: Universidade Federal de Uberlândia
Program: Programa de Pós-graduação em Ciência da Computação
Quote: BELIZÁRIO JÚNIOR, Clarivando Francisco. An approach to the personalized learning objects recommendation problem as a set covering problem using ontologies and metaheuristics. 2024. 140 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Uberlândia, Uberlândia, 2024. DOI http://doi.org/10.14393/ufu.te.2024.554.
Document identifier: http://doi.org/10.14393/ufu.te.2024.554
URI: https://repositorio.ufu.br/handle/123456789/43251
Date of defense: 29-Jul-2024
Sustainable Development Goals SDGs: ODS::ODS 4. Educação de qualidade - Assegurar a educação inclusiva, e equitativa e de qualidade, e promover oportunidades de aprendizagem ao longo da vida para todos.
Appears in Collections:TESE - Ciência da Computação

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