Recomendação Personalizada de Conteúdo Instrucional Complementar usando Repositório de Objetos de Aprendizagem e Recursos da Web
Resumo
Este artigo apresenta uma proposta de recomendação personalizada de Objetos de Aprendizagem (OA) na forma de conteúdo complementar (ou material adicional) a uma aula. Considerando que os repositórios educacionais são mais restritos que o conteúdo existente na Web, este trabalho também investiga a extensão de materiais, utilizando Wikipedia e Youtube para oferecer melhor suporte ao processo de aprendizagem do estudante. A proposta utiliza Filtragem Baseada em Conteúdo (FBC), levando em consideração preferências do estudante e metadados de uma aula. Os resultados apontam que o suporte de conteúdos externos gera melhor adaptatividade e diversificação no conteúdo instrucional entregue ao estudante.
Palavras-chave:
Recomendação de Objetos de Aprendizagem, Filtragem Baseada em Conteúdo, Conteúdo Educacional Complementar, Recursos Educacionais da Web
Referências
Almahairi, A., Kastner, K., Cho, K., and Courville, A. (2015). Learning distributed representations from reviews for collaborative filtering. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 147–154.
An, D. and Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116:410–416.
Araújo, R. D. (2017). Uma Arquitetura Computacional para Autoria e Personalização de Objetos de Aprendizagem em Ambientes Educacionais Ubíquos. PhD thesis, Universidade Federal de Uberlândia.
Araújo, R. D., Brant-Ribeiro, T., Ferreira, H. N., Dorça, F. A., and Cattelan, R. G. (2020).Using learning styles for creating and personalizing educational content in ubiquitous learning environments. Revista Brasileira de Informática na Educação, 28:133.
Curran, V., Simmons, K., Matthews, L., Fleet, L., Gustafson, D. L., Fairbridge, N. A., and Xu, X. (2020). Youtube as an educational resource in medical education: a scopingreview. Medical Science Educator, pages 1–8.
De Medio, C., Limongelli, C., Marani, A., and Taibi, D. (2019). Retrieval of educational resources from the web: A comparison between google and online educational repositories. In International Conference on Web-Based Learning, pages 28–38. Springer.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education.Engineering education, 78(7):674–681.
IEEE LTSC (2001). IEEE 1484.12.1 - Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Júnior, C. B. and Dorça, F. (2018). Uma abordagem para a criação e recomendação de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Simpósio Brasileiro de Informática na Educação.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Ljubojevic, M., Vaskovic, V., Stankovic, S., and Vaskovic, J. (2014). Using supplementary video in multimedia instruction as a teaching tool to increase efficiency of learning and quality of experience. International Review of Research in Open and Distributed Learning, 15(3):275–291.
Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook. Springer.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook, pages 387–415. Springer.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., and Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach.Telematics and Informatics, 34(5).
Selwyn, N. and Gorard, S. (2016). Students’ use of wikipedia as an academic resource-patterns of use and perceptions of usefulness. The Internet and Higher Education, 28.
Wan, S. and Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource.Knowledge-Based Systems, 160:71 – 87.
An, D. and Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116:410–416.
Araújo, R. D. (2017). Uma Arquitetura Computacional para Autoria e Personalização de Objetos de Aprendizagem em Ambientes Educacionais Ubíquos. PhD thesis, Universidade Federal de Uberlândia.
Araújo, R. D., Brant-Ribeiro, T., Ferreira, H. N., Dorça, F. A., and Cattelan, R. G. (2020).Using learning styles for creating and personalizing educational content in ubiquitous learning environments. Revista Brasileira de Informática na Educação, 28:133.
Curran, V., Simmons, K., Matthews, L., Fleet, L., Gustafson, D. L., Fairbridge, N. A., and Xu, X. (2020). Youtube as an educational resource in medical education: a scopingreview. Medical Science Educator, pages 1–8.
De Medio, C., Limongelli, C., Marani, A., and Taibi, D. (2019). Retrieval of educational resources from the web: A comparison between google and online educational repositories. In International Conference on Web-Based Learning, pages 28–38. Springer.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education.Engineering education, 78(7):674–681.
IEEE LTSC (2001). IEEE 1484.12.1 - Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Júnior, C. B. and Dorça, F. (2018). Uma abordagem para a criação e recomendação de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Simpósio Brasileiro de Informática na Educação.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Ljubojevic, M., Vaskovic, V., Stankovic, S., and Vaskovic, J. (2014). Using supplementary video in multimedia instruction as a teaching tool to increase efficiency of learning and quality of experience. International Review of Research in Open and Distributed Learning, 15(3):275–291.
Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook. Springer.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook, pages 387–415. Springer.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., and Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach.Telematics and Informatics, 34(5).
Selwyn, N. and Gorard, S. (2016). Students’ use of wikipedia as an academic resource-patterns of use and perceptions of usefulness. The Internet and Higher Education, 28.
Wan, S. and Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource.Knowledge-Based Systems, 160:71 – 87.
Publicado
24/11/2020
Como Citar
PEREIRA JÚNIOR, Cleon; ARAÚJO, Rafael D.; DORÇA, Fabiano A..
Recomendação Personalizada de Conteúdo Instrucional Complementar usando Repositório de Objetos de Aprendizagem e Recursos da Web. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1293-1302.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1293.
