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 |
Files in This Item:
File | Description | Size | Format | |
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ApproachPersonalizedLearning.pdf | Tese | 24.75 MB | Adobe PDF | View/Open |
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