Análise de modelo para a tomada de decisões pedagógicas em um ambiente voltado ao aprendizado de algoritmos
Resumo
O presente trabalho analisa de forma qualitativa o emprego de POMDP como modelo para a tomada de decisões pedagógicas em um ambiente voltado ao aprendizado de algoritmos. A questão a ser enfrentada é o sequenciamento de problemas computacionais em ambientes de tutoria inteligente, tendo em vista ser uma prática comum a proposição de problemas computacionais em cursos introdutórios de algoritmos. O sequenciamento das atividades foi encarado como um processo de decisão sequencial sob incerteza, vez que o tutor não sabe exatamente em qual estado de conhecimento o aluno está atualmente. A principal contribuição deste trabalho foi mostrar que o modelo pode ser aplicado de forma satisfatória ao processo de tutoria inteligente, tendo em vista que os alunos foram guiados pelos caminhos que ofereciam os maiores ganhos de conhecimento.
Palavras-chave:
Decisões Pedagógicas, Aprendizado de Algoritmos, POMDP
Referências
Bez, J. L., Tonin, N. A., and Rodegheri, P. R. (2014). Uri online judge academic: A tool for algorithms and programming classes. In 2014 9th International Conference on Computer Science Education, pages 149–152.
Kolobov, A. (2013).Scalable methods and expressive models for planning under uncer-tainty. PhD thesis.
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Mostr ̈om, J. E., Sanders, K., Sepp ̈al ̈a, O., et al. (2004). A multi-national study of reading and tracing skills in novice programmers.ACM SIGCSE Bulletin, 36(4):119–150.
McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.-D.,Laxer, C., Thomas, L., Utting, I., and Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year cs students. In Working group reports from ITiCSE on Innovation and technology in computer science education, pages 125–180.
Pineau, J., Gordon, G., Thrun, S., et al. (2003). Point-based value iteration: An anytime algorithm for pomdps. In IJCAI, volume 3, pages 1025–1032.
Rafferty, A. N., Brunskill, E., Griffiths, T. L., and Shafto, P. (2011). Faster teaching by pomdp planning. InInternational Conference on Artificial Intelligence in Education, pages 280–287. Springer.
Revilla, M. A., Manzoor, S., and Liu, R. (2008). Competitive learning in informatics:The uva online judge experience. Olympiads in Informatics, 2(10):131–148.
Smallwood, R. D. and Sondik, E. J. (1973). The optimal control of partially observable markov processes over a finite horizon. Operations research, 21(5):1071–1088.
Somani, A., Ye, N., Hsu, D., and Lee, W. S. (2013). Despot: Online pomdp planning with regularization. In Advances in neural information processing systems, pages 1772–1780.
Ten Pas, A. (2012). Simulation based planning for partially observable markov decision processes with continuous observation spaces. Master’s thesis, Faculty of Humanities and Sciences, Maastricht University.
Theocharous, G., Beckwith, R., Butko, N., and Philipose, M. (2009). Tractable pomdp planning algorithms for optimal teaching in “spais”. In IJCAI PAIR Workshop.
Van Otterlo, M. and Wiering, M. (2012). Reinforcement learning and markov decision processes. In Reinforcement Learning, pages 3–42. Springer.
Wang, F. (2018). Reinforcement learning in a pomdp based intelligent tutoring system for optimizing teaching strategies. International Journal of Information and Education Technology, 8(8).
Woolf, B. P. (2010).Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Zhang, P. (2013).Using POMDP-based reinforcement learning for online optimization of teaching strategies in an intelligent tutoring system. PhD thesis.
Kolobov, A. (2013).Scalable methods and expressive models for planning under uncer-tainty. PhD thesis.
Lister, R., Adams, E. S., Fitzgerald, S., Fone, W., Hamer, J., Lindholm, M., McCartney, R., Mostr ̈om, J. E., Sanders, K., Sepp ̈al ̈a, O., et al. (2004). A multi-national study of reading and tracing skills in novice programmers.ACM SIGCSE Bulletin, 36(4):119–150.
McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.-D.,Laxer, C., Thomas, L., Utting, I., and Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year cs students. In Working group reports from ITiCSE on Innovation and technology in computer science education, pages 125–180.
Pineau, J., Gordon, G., Thrun, S., et al. (2003). Point-based value iteration: An anytime algorithm for pomdps. In IJCAI, volume 3, pages 1025–1032.
Rafferty, A. N., Brunskill, E., Griffiths, T. L., and Shafto, P. (2011). Faster teaching by pomdp planning. InInternational Conference on Artificial Intelligence in Education, pages 280–287. Springer.
Revilla, M. A., Manzoor, S., and Liu, R. (2008). Competitive learning in informatics:The uva online judge experience. Olympiads in Informatics, 2(10):131–148.
Smallwood, R. D. and Sondik, E. J. (1973). The optimal control of partially observable markov processes over a finite horizon. Operations research, 21(5):1071–1088.
Somani, A., Ye, N., Hsu, D., and Lee, W. S. (2013). Despot: Online pomdp planning with regularization. In Advances in neural information processing systems, pages 1772–1780.
Ten Pas, A. (2012). Simulation based planning for partially observable markov decision processes with continuous observation spaces. Master’s thesis, Faculty of Humanities and Sciences, Maastricht University.
Theocharous, G., Beckwith, R., Butko, N., and Philipose, M. (2009). Tractable pomdp planning algorithms for optimal teaching in “spais”. In IJCAI PAIR Workshop.
Van Otterlo, M. and Wiering, M. (2012). Reinforcement learning and markov decision processes. In Reinforcement Learning, pages 3–42. Springer.
Wang, F. (2018). Reinforcement learning in a pomdp based intelligent tutoring system for optimizing teaching strategies. International Journal of Information and Education Technology, 8(8).
Woolf, B. P. (2010).Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Zhang, P. (2013).Using POMDP-based reinforcement learning for online optimization of teaching strategies in an intelligent tutoring system. PhD thesis.
Publicado
24/11/2020
Como Citar
MARANHÃO, Djefferson S. S.; RAPOSO, Antonio Carlos; SOARES NETO, Carlos de Salles.
Análise de modelo para a tomada de decisões pedagógicas em um ambiente voltado ao aprendizado de algoritmos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1223-1232.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1223.
