OSM-V: um Modelo Aberto de Estudante para Visualização de Desempenho em Sistemas Adaptativos e Inteligentes para Educação
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https://doi.org/10.5753/cbie.sbie.2018.1333
Resumen
Um dos grandes desafios da computação aplicada à educação está na capacidade de apoiar ambientes que sejam inteligentes e adaptáveis às necessidades dos estudantes. Para isso, é preciso criar Modelos de Estudantes eficazes, com o intuito de identificar e predizer o nível de conhecimento de cada estudante. Além disso, é importante que os próprios estudantes saibam seu nível de conhecimento em um dado instante, para com isso, poderem autorregular seu processo de aprendizagem. Assim, este trabalho descreve o OSM-V, um Modelo Aberto de Estudante com recursos de Visualização de Informação e que permite que estudantes e instrutores tenham acesso às informações inferidas em Sistemas Adaptativos e Inteligentes para Educação. Além da descrição do modelo, este artigo também apresenta experimentos, aplicados a estudantes reais, com o objetivo de avaliar a percepção de utilidade e mudanças comportamentais nos estudantes que utilizaram a ferramenta. Resultados mostraram que esta proposta pode impactar positivamente na aprendizagem dos estudantes e também influenciar positivamente seus comportamentos.
Palabras clave:
modelo aberto de estudante, visualização de informação, sistemas adaptativos de educação, aprendizagem autorregulada, educação inteligente
Citas
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Long, Y. and Aleven, V. (2013). Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In International Conference on Artificial Intelligence in Education, pages 219–228. Springer.
Long, Y. and Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27(1):55–88.
Mabbott, A. and Bull, S. (2006). Student Preferences for Editing, Persuading, and Negotiating the Open Learner Model, pages 481–490. Springer, Berlin, Heidelberg.
Mitrovic, A. and Martin, B. (2002). Evaluating the Effects of Open Student Models on Learning, pages 296–305. Springer Berlin Heidelberg, Berlin, Heidelberg.
Mitrovic, A. and Martin, B. (2007). Evaluating the effect of open student models on self-assessment. Int. Journal of Artificial Intelligence in Education, 17(2):121–144.
Mitrovic, A. and Thomson, D. (2009). Towards a negotiable student model for constraintbased itss.
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., and Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, pages 703 – 714.
Self, J. A. (1990). Bypassing the intractable problem of student modelling. Intelligent tutoring systems: At the crossroads of artificial intelligence and education, 41:1–26.
Bull, S. and Kay, J. (2013). Open Learner Models as Drivers for Metacognitive Processes, pages 349–365. Springer, New York, NY.
Bull, S. and Wasson, B. (2016). Competence visualisation: Making sense of data from 21 st-century technologies in language learning. ReCALL, 28(02):147–165.
Ferreira, H. N. M., Araújo, R. D., Dorça, F., and Cattelan, R. (2017). Uma abordagem baseada em ontologias para modelagem e avaliação do estudante em sistemas adaptativos e inteligentes para educação. In XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017), number 1, pages 1197–1206.
Ginon, B., Boscolo, C., Johnson, M. D., and Bull, S. (2016). Persuading an Open Learner Model in the Context of a University Course: An Exploratory Study, pages 307–313. Springer International Publishing, Cham.
Greiff, S., Niepel, C., Scherer, R., and Martin, R. (2016). Understanding students’ performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Comp. in Human Beh., 61:36–46.
Guerra, J., Hosseini, R., Somyurek, S., and Brusilovsky, P. (2016). An intelligent interface for learning content: Combining an open learner model and social comparison to support self-regulated learning and engagement. In Proc. of the 21st International Conference on Intelligent User Interfaces, IUI ’16, pages 152–163, NY, USA. ACM.
Hartley, D. and Mitrovic, A. (2002). Supporting Learning by Opening the Student Model, pages 453–462. Springer Berlin Heidelberg.
Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and K̈onig-Ries, B. (2013). Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2):112–131.
Jacovina, M. E., Snow, E. L., Allen, L. K., Roscoe, R. D., Weston, J. L., Dai, J., and McNamara, D. S. (2015). How to visualize success: Presenting complex data in a writing strategy tutor. In Proc. of the 8th International Conf. on Educational Data Mining, EDM, pages 594–595.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1).
Li, N., Cohen, W. W., Koedinger, K. R., and Matsuda, N. (2011). A machine learning approach for automatic student model discovery. In Proceedings of the 4th International Conference on Educational Data Mining, pages 31–40.
Lindstaedt, S. N., Beham, G., Kump, B., and Ley, T. (2009). Getting to Know Your User – Unobtrusive User Model Maintenance within Work-Integrated Learning Environments, pages 73–87. Springer Berlin Heidelberg, Berlin, Heidelberg.
Long, Y. and Aleven, V. (2013). Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In International Conference on Artificial Intelligence in Education, pages 219–228. Springer.
Long, Y. and Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27(1):55–88.
Mabbott, A. and Bull, S. (2006). Student Preferences for Editing, Persuading, and Negotiating the Open Learner Model, pages 481–490. Springer, Berlin, Heidelberg.
Mitrovic, A. and Martin, B. (2002). Evaluating the Effects of Open Student Models on Learning, pages 296–305. Springer Berlin Heidelberg, Berlin, Heidelberg.
Mitrovic, A. and Martin, B. (2007). Evaluating the effect of open student models on self-assessment. Int. Journal of Artificial Intelligence in Education, 17(2):121–144.
Mitrovic, A. and Thomson, D. (2009). Towards a negotiable student model for constraintbased itss.
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., and Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, pages 703 – 714.
Self, J. A. (1990). Bypassing the intractable problem of student modelling. Intelligent tutoring systems: At the crossroads of artificial intelligence and education, 41:1–26.
Publicado
29/10/2018
Cómo citar
FERREIRA, Hiran N. M.; OLIVEIRA, Guilherme P.; ARAÚJO, Rafael D.; DORÇA, Fabiano A.; CATTELAN, Renan G..
OSM-V: um Modelo Aberto de Estudante para Visualização de Desempenho em Sistemas Adaptativos e Inteligentes para Educação. In: ACTAS DEL SIMPOSIO BRASILEÑO DE INFORMÁTICA EN LA EDUCACIÓN (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
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p. 1333-1342.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1333.
