A Semiautomatic and Probabilistic Approach for Student Modeling in Ubiquitous Learning Environments
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https://doi.org/10.5753/cbie.sbie.2018.1313
Resumen
Ambientes Educacionais Ubíquos (AEUs) potencializam a geração automática de Objetos de Aprendizagem (OAs) que, por sua vez, necessitam de estratégias mais adequadas de apresentação. Para atender as diferentes necessidades e preferências dos estudantes, recursos de recuperação e personalização têm sido explorados, agregando teorias cognitivas ao modelo do estudante. Este artigo apresenta uma proposta probabilística do modelo de Felder e Silverman (FSLSM) que inclui uma abordagem semiautomática para sua aferição em AEUs. Resultados preliminares indicam a existência de correlações entre diferentes tipos de interações realizadas pelos estudantes e seus respectivos estilos de aprendizagem.
Palabras clave:
estilos de aprendizagem, ambientes de aprendizagem ubíquos, modelagem de estudantes, Felder-Silverman Learning Style Model, personalização da aprendizagem
Citas
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Ahmad, N., Tasir, Z., Kasim, J., and Sahat, H. (2013). Automatic detection of learning styles in learning management systems by using literature-based method. Procedia - Social and Behavioral Sciences, 103:181–189.
Alshammari, M., Anane, R., and Hendley, R. J. (2015). The impact of learning style adaptivity in teaching computer security. In Proc. of the 2015 ACM Conf. on Innovation and Technology in Computer Science Education, ITiCSE’15, pages 135–140.
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., Brant-Ribeiro, T., Ferreira, H. N. M., Dorça, F. A., and Cattelan, R. G. (2016). Segmentação Colaborativa de Objetos de Aprendizagem Utilizando Bookmarks em Ambientes Educacionais Ubíquos. In Proc. of the 27th SBIE, 2016, pages 1205–1214.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1):87–110.
Davis, J. A. (1971). Elementary survey analysis. Englewood Cliffs, NJ, Prentice-Hall.
Dorca, F. A., Lima, L. V., Fernandes, M. A., and Lopes, C. R. (2013). Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Systems with Applications, 40(6):2092–2101.
El-Bishouty, M. M., Chang, T.-W., Graf, S., Kinshuk, and Chen, N.-S. (2014). Smart e-course recommender based on learning styles. Journal of Computers in Education, 1(1):99–111.
Essalmi, F., Ayed, L. J. B., Jemni, M., Graf, S., and Kinshuk (2015). Generalized metrics for the analysis of e-learning personalization strategies. Computers in Human Behavior, 48:310–322.
Fasihuddin, H., Skinner, G., and Athauda, R. (2016). Using learning styles as a basis for creating adaptive open learning environments: an evaluation. International Journal of Learning Technology, 11(3):198–217.
Felder, R. M. and Silverman, L. K. (1988). Learning and Teaching Styles in Engineering Education. Journal of Engineering Education, 78(7):674–681.
Felder, R. M. and Spurlin, J. (2005). Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education, 21(1):103–112.
Ferreira, H. N. M., Brant-Ribeiro, T., Araújo, R. D., Dorça, F. A., and Cattelan, R. G. (2016). An automatic and dynamic student modeling approach for adaptive and intelligent educational systems using ontologies and Bayesian networks. In 2016 IEEE 28th International Conf. on Tools with Artificial Intelligence (ICTAI), pages 738–745.
Graf, S., Chang, T. W., Kersebaum, A., Rath, T., and Kurcz, J. (2014). Investigating the effectiveness of an advanced adaptive mechanism for considering learning styles in learning management systems. In Proc. of the IEEE 14th International Conf. on Advanced Learning Technologies, pages 112–116.
Graf, S., Kinshuk, and Liu, T.-C. (2008). Identifying learning styles in learning management systems by using indications from students’ behaviour. In Proc. of the 2008 Eighth IEEE International Conf. on Advanced Learning Technologies, pages 482–486.
IEEE (2002). LTSC 1484.12.1, Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Moser, S. and Zumbach, J. (2018). Exploring the development and impact of learning styles: An empirical investigation based on explicit and implicit measures. Computers & Education, 125:146–157.
Mühlbeier, A. and Mozzaquatro, P. (2012). Estilos e estratégias de aprendizagem personalizadas a alunos das modalidades presenciais e a distância. Revista Brasileira de Informática na Educação, 20(1):132–139.
Pimentel, M. d. G., Ishiguro, Y., Kerimbaev, B., Abowd, G. D., and Guzdial, M. (2001). Supporting Educational Activities through Dynamic Web Interfaces. Interacting with Computers, Special Issue on Interacting with the Active Web, 13(3):353–375.
Sena, E., Vivas, A., Assis, L., and Pitangui, C. (2016). Proposta de uma abordagem computacional para detecção automática de estilos de aprendizagem utilizando modelos ocultos de Markov e FSLSM. In Proc. of the 27th SBIE, 2016, pages 1126–1135.
Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3):66–75.
Yang, T.-C., Hwang, G.-J., and Yang, S. J.-H. (2013). Development of an Adaptive Learning System with Multiple Perspectives based on Students’ Learning Styles and Cognitive Styles. Educational Technology & Society, 16(4):185–200.
Publicado
29/10/2018
Cómo citar
ARAÚJO, Rafael D.; BRANT-RIBEIRO, Taffarel; FERREIRA, Hiran N. M.; DORÇA, Fabiano A.; CATTELAN, Renan G..
A Semiautomatic and Probabilistic Approach for Student Modeling in Ubiquitous Learning Environments. In: ACTAS DEL SIMPOSIO BRASILEÑO DE INFORMÁTICA EN LA EDUCACIÓN (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1313-1322.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1313.
