Um Arcabouço Adaptativo para Objetos de Aprendizagem
Resumo
Este trabalho aborda questões teóricas e de implementação de um arcabouço para a construção e execução de Objetos de Aprendizagem (OAs), em que as tarefas de resolução de problemas são ordenadas de acordo com o emparelhamento de 2 parâmetros formalmente definidos como expressões algébricas:(1) nível de expertise do aluno e (2) dificuldade de solução de um problema. O nível de habilidade é expresso por um rating, semelhante aos usados em jogos. O cálculo da dificuldade de solução é baseado em erros e acertos de estudantes ao lidar com o problema. São apresentados resultados de um experimento realizado com quatro turmas do ensino médio de uma escola pública, utilizando um OA construído com o arcabouço para o domínio de logaritmos.
Palavras-chave:
Objetos de Aprendizagem, Aprendizagem Adaptativa
Referências
Baker, F. B. (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Wiscosin.
Baker, R. S., Goldstein, A. B., and Heffernan, N. T. (2010). Detecting the Moment of Learning. LNCS, 6094(PART I):25–34. Springer-Verlag Berlin Heidelberg.
Champaign, J. and Cohen, R. (2010). A Model for Content Sequencing in Intelligent Tutorign Systems Based on the Ecological Approach and Its Validation Through Simulated Students. pages 486–491. Association for the Advancement of Artificial Intelligence (AAAI).
Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4):253–278.
Direne, A. (1990). Authoring Intelligent Systems for Teaching Visual Concepts. International Journal of Artificial Intelligence in Education, 1(4):3–14.
Isotani, S., Adams, D., Mayer, R. E., Durkin, K., Rittle-Hohnson, B., and McLaren, B. M. (2011). Can Erroneous Examples Help Middle-School Students Learn Decimals? volume 6964 of Lecture Notes in Computer Science, pages 181–195, Palermo. Springer Berlin Heidelberg.
Major, N. and Reichgelt, H. (1992). COCA: A Shell for Intelligent Tutoring Systems. In Proc. of the International Conference on Intelligent Tutoring Systems (ITS92), pages 523–530. Springer.
Marczal, D. (2014). Farma: Uma ferramenta de autoria para objetos de aprendizagem de conceitos matemáticos. Tese de doutorado, Universidade Federal do Paraná, UFPR.
Marczal, D. and Direne, A. (2012). FARMA: Uma Ferramenta de Autoria para Objetos de Aprendizagem de Conceitos Matemáticos. In Anais do Simpósio Brasileiro de Informática na Educação, volume 23.
Mitrovic, A. (2003). An intelligent SQL tutor on the web. International Journal of Artificial Intelligence in Education, 13(3):173–197.
Pinkwart, N. and Loll, F. (2009). Comparing three approaches to assess the quality of students’ solutions. In Dicheva, D., Mizoguchi, R., and Pinkwart, N., editors, AIED 2009 Workshops Proceedings Volume 2, SWEL’09: Ontologies and Social Semantic Web for Intelligent Educational Systems Intelligent Educational Games, pages 81–85.
Ravi, G. A. and Sosnovsky, S. (2013). Exercise difficulty Calibration Based on Student Log Mining. In Mödritscher, F., Luengo, V., Law, E. L.-C., and Hoppe, U., editors, Proceedings of DAILE’13: Workshop on Data Analysis and Interpretation for Learning Environments, Villard-de-Lans (France).
Ritter, S., Anderson, J. R., Koedinger, K. R., and Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic bulletin & review, 14(2):249–255.
Schatten, C. and Schmidt-Thieme, L. (2014). Adaptive Content Sequencing without Domain Information. 6th International Conference on Computer based Education.
Segal, A., Katzir, Z., Gal, K., Shani, G., and Shapira, B. (2014). EduRank: A Collaborative Filtering Approach to Personalization in E-learning. In Stamper, J., P. Z. M. M. M. B., editor, Proceedings of the 7th International Conference on Educational Data Mining, pages 68–75.
Self, J. (1990). Bypassing the Intractable Problem of Student Modelling. (41):1–26. AAI/AI-ED.
Baker, R. S., Goldstein, A. B., and Heffernan, N. T. (2010). Detecting the Moment of Learning. LNCS, 6094(PART I):25–34. Springer-Verlag Berlin Heidelberg.
Champaign, J. and Cohen, R. (2010). A Model for Content Sequencing in Intelligent Tutorign Systems Based on the Ecological Approach and Its Validation Through Simulated Students. pages 486–491. Association for the Advancement of Artificial Intelligence (AAAI).
Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4):253–278.
Direne, A. (1990). Authoring Intelligent Systems for Teaching Visual Concepts. International Journal of Artificial Intelligence in Education, 1(4):3–14.
Isotani, S., Adams, D., Mayer, R. E., Durkin, K., Rittle-Hohnson, B., and McLaren, B. M. (2011). Can Erroneous Examples Help Middle-School Students Learn Decimals? volume 6964 of Lecture Notes in Computer Science, pages 181–195, Palermo. Springer Berlin Heidelberg.
Major, N. and Reichgelt, H. (1992). COCA: A Shell for Intelligent Tutoring Systems. In Proc. of the International Conference on Intelligent Tutoring Systems (ITS92), pages 523–530. Springer.
Marczal, D. (2014). Farma: Uma ferramenta de autoria para objetos de aprendizagem de conceitos matemáticos. Tese de doutorado, Universidade Federal do Paraná, UFPR.
Marczal, D. and Direne, A. (2012). FARMA: Uma Ferramenta de Autoria para Objetos de Aprendizagem de Conceitos Matemáticos. In Anais do Simpósio Brasileiro de Informática na Educação, volume 23.
Mitrovic, A. (2003). An intelligent SQL tutor on the web. International Journal of Artificial Intelligence in Education, 13(3):173–197.
Pinkwart, N. and Loll, F. (2009). Comparing three approaches to assess the quality of students’ solutions. In Dicheva, D., Mizoguchi, R., and Pinkwart, N., editors, AIED 2009 Workshops Proceedings Volume 2, SWEL’09: Ontologies and Social Semantic Web for Intelligent Educational Systems Intelligent Educational Games, pages 81–85.
Ravi, G. A. and Sosnovsky, S. (2013). Exercise difficulty Calibration Based on Student Log Mining. In Mödritscher, F., Luengo, V., Law, E. L.-C., and Hoppe, U., editors, Proceedings of DAILE’13: Workshop on Data Analysis and Interpretation for Learning Environments, Villard-de-Lans (France).
Ritter, S., Anderson, J. R., Koedinger, K. R., and Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic bulletin & review, 14(2):249–255.
Schatten, C. and Schmidt-Thieme, L. (2014). Adaptive Content Sequencing without Domain Information. 6th International Conference on Computer based Education.
Segal, A., Katzir, Z., Gal, K., Shani, G., and Shapira, B. (2014). EduRank: A Collaborative Filtering Approach to Personalization in E-learning. In Stamper, J., P. Z. M. M. M. B., editor, Proceedings of the 7th International Conference on Educational Data Mining, pages 68–75.
Self, J. (1990). Bypassing the Intractable Problem of Student Modelling. (41):1–26. AAI/AI-ED.
Publicado
26/10/2015
Como Citar
SILVA, Rômulo C.; DIRENE, Alexandre I.; MARCZAL, Diego; BORILLE, Ana Carla; GUIMARÃES, Paulo R. B.; CABRAL, Ângelo S.; CAMARGO, Bruno F..
Um Arcabouço Adaptativo para Objetos de Aprendizagem. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 26. , 2015, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 1032-1041.
DOI: https://doi.org/10.5753/cbie.sbie.2015.1032.
