Uma Abordagem Baseada em Ontologias para Modelagem e Avaliação do Estudante em Sistemas Adaptativos e Inteligentes para Educação
Resumo
Novas tecnologias surgem diariamente com o intuito de auxiliar na criação de ferramentas cada vez mais adaptáveis às particularidades de cada usuário. Um dos recursos computacionais que busca melhorar esses processos são os Sistemas Adaptativos e Inteligentes para Educação (SAIE)s, que possuem a capacidade de alterar suas características para atender às necessidades individuais de cada estudante. Este artigo propõe uma abordagem baseada em ontologias para a modelagem e avaliação do estudante em SAIEs. A principal função desse modelo é acompanhar as atividades desenvolvidas pelo estudante e determinar um nível de desempenho com base no seu conhecimento e comportamento. Experimentos com estudantes reais foram desenvolvidos, no qual foi possível identificar os principais indicadores para avaliar e acompanhar o seu progresso durante a utilização de um ambiente virtual.
Palavras-chave:
Ontologias, Modelagem de Estudantes, Sistemas Adaptativos e Inteligentes para Educação
Referências
Almeida, M.B. e Bax, M.P. (2003). Uma visão geral sobre ontologias: Pesquisas sobre definições, tipos, aplicações, métodos de avaliação e de construção. Ciência da Informação, Brasília, 32(3):7–20.
Bautista-Zambrana, M.R. (2015). Methodologies to Build Ontologies for Terminological Purposes. Procedia-Social and Behavioral Sciences, 173:264–269.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1-2):87–110.
Chrysafiadi, K. e Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11):4715–4729.
Clemente, J., Ramírez, J., e de Antonio, A. (2011). A proposal for student modeling based on ontologies and diagnosis rules. Expert Systems with Applications, 38(7):8066–8078.
Dolog, P. e Nejdl, W. (2007). The adaptive web. chapter Semantic Web Technologies for the Adaptive Web, pages 697–719. Springer, Berlin, Heidelberg.
Ferreira, H.N.M., Brant-Ribeiro, T., Araujo, R.D., Dorça, F.A., e Cattelan, R.G. (2016). An automatic and dynamic student modeling approach for adaptive and intelligent educational systems using ontologies and Bayesian networks. In Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016, pages 738–745.
Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2):199–220.
Hsiao, I.-H., Bakalov, F., Brusilovsky, P., e König-Ries, B. (2013). Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2):112–131.
Isotani, S. e Bittencourt, I.I. (2015). Dados Abertos Conectados: Em busca da Web do Conhecimento. Novatec Editora.
Luckin, R. e Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
Mahnane, L. e Laskri, M.T. (2012). An adaptive hypermedia system integrating thinking style (ahs-ts): Model and experiment. International Journal of Hybrid Information Technology, 5(1):11–28.
Minović, M., Milovanović, M., Šošević, U., e González, M. Á.C. (2015). Visualisation of student learning model in serious games. Computers in Human Behavior, 47:98–107.
Nafea, S., Maglaras, L., Siewe, F., Smith, R., e Janicke, H. (2016). Personalized students’ profile based on ontology and rule-based reasoning. ICST Transactions 3:1–14.
Nguyen, C.D., Vo, K.D., Bui, D.B., e Nguyen, D.T. (2011). An ontology-based IT student model in an educational social network. In Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, iiWAS’11, pages 379–382, ACM.
Ouf, S., Ellatif, M.A., Salama, S., e Helmy, Y. (2017). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior, 72:796–818.
Panagiotopoulos, I., Kalou, A., Pierrakeas, C., e Kameas, A. (2012). An ontology-based model for student representation in intelligent tutoring systems for distance learning. Artificial Intelligence Applications and Innovations, pages 296–305.
Pukkhem, N. (2013). Ontology-based semantic approach for learning object recommendation. International Journal on Information Technology, 3(4):12.
Rezende, P.A., Pereira, C., Campos, F., David, J., e Braga, R. (2015). Personna: proposta de ontologia de contexto e perfil de alunos para recomendação de objetos de aprendizagem. Revista Brasileira de Informática na Educação, 23(01):70.
Shute, V.J., D’Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M., e Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86:224–235.
Siemens, G. e Baker, R.S.J.d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge, LAK’12, pages 252–254, ACM.
Suárez-Figueroa, M.C., de Cea, G.A., Buil, C., Dellschaft, K., Fernández-López, M., García, A., Gómez-Pérez, A., Herrero, G., Montiel-Ponsoda, E., Sabou, M., et al. (2008). D5.4.1. neon methodology for building contextualized ontology networks. NeOn Project Deliverable D, 5.
Ting, C.-Y. e Phon-Amnuaisuk, S. (2012). Properties of Bayesian student model for inq-pro. Applied Intelligence, 36(2):391–406.
Uschold, M. e Gruninger, M. (1996). Ontologies: Principles, methods and applications. Knowledge Engineering Review, 11:93–136.
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., e Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies, 5(4):318–335.
Yu, L. (2011). A developer’s guide to the semantic Web. Springer Science & Business Media.
Bautista-Zambrana, M.R. (2015). Methodologies to Build Ontologies for Terminological Purposes. Procedia-Social and Behavioral Sciences, 173:264–269.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1-2):87–110.
Chrysafiadi, K. e Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11):4715–4729.
Clemente, J., Ramírez, J., e de Antonio, A. (2011). A proposal for student modeling based on ontologies and diagnosis rules. Expert Systems with Applications, 38(7):8066–8078.
Dolog, P. e Nejdl, W. (2007). The adaptive web. chapter Semantic Web Technologies for the Adaptive Web, pages 697–719. Springer, Berlin, Heidelberg.
Ferreira, H.N.M., Brant-Ribeiro, T., Araujo, R.D., Dorça, F.A., e Cattelan, R.G. (2016). An automatic and dynamic student modeling approach for adaptive and intelligent educational systems using ontologies and Bayesian networks. In Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016, pages 738–745.
Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2):199–220.
Hsiao, I.-H., Bakalov, F., Brusilovsky, P., e König-Ries, B. (2013). Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2):112–131.
Isotani, S. e Bittencourt, I.I. (2015). Dados Abertos Conectados: Em busca da Web do Conhecimento. Novatec Editora.
Luckin, R. e Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.
Mahnane, L. e Laskri, M.T. (2012). An adaptive hypermedia system integrating thinking style (ahs-ts): Model and experiment. International Journal of Hybrid Information Technology, 5(1):11–28.
Minović, M., Milovanović, M., Šošević, U., e González, M. Á.C. (2015). Visualisation of student learning model in serious games. Computers in Human Behavior, 47:98–107.
Nafea, S., Maglaras, L., Siewe, F., Smith, R., e Janicke, H. (2016). Personalized students’ profile based on ontology and rule-based reasoning. ICST Transactions 3:1–14.
Nguyen, C.D., Vo, K.D., Bui, D.B., e Nguyen, D.T. (2011). An ontology-based IT student model in an educational social network. In Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, iiWAS’11, pages 379–382, ACM.
Ouf, S., Ellatif, M.A., Salama, S., e Helmy, Y. (2017). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior, 72:796–818.
Panagiotopoulos, I., Kalou, A., Pierrakeas, C., e Kameas, A. (2012). An ontology-based model for student representation in intelligent tutoring systems for distance learning. Artificial Intelligence Applications and Innovations, pages 296–305.
Pukkhem, N. (2013). Ontology-based semantic approach for learning object recommendation. International Journal on Information Technology, 3(4):12.
Rezende, P.A., Pereira, C., Campos, F., David, J., e Braga, R. (2015). Personna: proposta de ontologia de contexto e perfil de alunos para recomendação de objetos de aprendizagem. Revista Brasileira de Informática na Educação, 23(01):70.
Shute, V.J., D’Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M., e Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86:224–235.
Siemens, G. e Baker, R.S.J.d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge, LAK’12, pages 252–254, ACM.
Suárez-Figueroa, M.C., de Cea, G.A., Buil, C., Dellschaft, K., Fernández-López, M., García, A., Gómez-Pérez, A., Herrero, G., Montiel-Ponsoda, E., Sabou, M., et al. (2008). D5.4.1. neon methodology for building contextualized ontology networks. NeOn Project Deliverable D, 5.
Ting, C.-Y. e Phon-Amnuaisuk, S. (2012). Properties of Bayesian student model for inq-pro. Applied Intelligence, 36(2):391–406.
Uschold, M. e Gruninger, M. (1996). Ontologies: Principles, methods and applications. Knowledge Engineering Review, 11:93–136.
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., e Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies, 5(4):318–335.
Yu, L. (2011). A developer’s guide to the semantic Web. Springer Science & Business Media.
Publicado
30/10/2017
Como Citar
FERREIRA, Hiran N. M.; ARAÚJO, Rafael D.; DORÇA, Fabiano A.; CATTELAN, Renan G..
Uma Abordagem Baseada em Ontologias para Modelagem e Avaliação do Estudante em Sistemas Adaptativos e Inteligentes para Educação. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 28. , 2017, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1197-1206.
DOI: https://doi.org/10.5753/cbie.sbie.2017.1197.
