Analisando a relação de conceitos de programação com o comportamento ”abuso do sistema” em programadores novatos
Resumo
Em ambientes interativos de aprendizagem, os aprendizes podem escolher interagir de diversas maneiras, principalmente em atividades de resolução de problemas. Neste contexto, observa-se que, alguns aprendizes recorrem às facilidades de suporte do sistema com um comportamento inadequado , identificado na literatura, como gaming the system– optamos por traduzir para ”abuso do sistema”.Assim, neste artigo, apresentamos um modelo para detectar o comportamento de ”abuso do sistema”, utilizando algoritmos de aprendizagem de máquina. Assim, após a detecção de tal comportamento, analisamos a relação de 16 conceitos, geralmente, abordados de programação introdutória. Com isso, nossos resultados mostram que os casos de ”abuso do sistema”acontecem em maioria nos problemas associados a controle de fluxo e que os aprendizes consideram mais difíceis.
Referências
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Aleven, V., Mclaren, B., Roll, I., and Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16(2):101–128.
Baker, R. and de Carvalho, A. (2008). Labeling student behavior faster and more precisely with text replays. In Educational Data Mining 2008.
Baker, R. S., Corbett, A. T., and Koedinger, K. R. (2004a). Detecting student misuse of intelligent tutoring systems. In International conference on intelligent tutoring systems, pages 531–540. Springer.
Baker, R. S., Corbett, A. T., Koedinger, K. R., and Wagner, A. Z. (2004b). Off-task behavior in the cognitive tutor classroom: When students”game the system”. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 383–390.
Baker, R. S., Roll, I., Corbett, A. T., and Koedinger, K. R. (2005). Do performance goals lead students to game the system? In AIED, pages 57–64.
Beck, J. E. and Mostow, J. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In International conference on intelligent tutoring systems, pages 353–362. Springer.
Brown, N. C. and Altadmri, A. (2017). Novice java programming mistakes: Largescale data vs. educator beliefs. ACM Transactions on Computing Education (TOCE), 17(2):1–21.
Cavalcanti, A. P., de Mello, R. F. L., de Miranda, P. B. C., and de Freitas, F. L. G. (2020). Análise automática de feedback em ambientes de aprendizagem online. In Anais do XXXI Simpósio Brasileiro de Informática na Educaçao, pages 892–901. SBC.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794.
Cheng, R. and Vassileva, J. (2005). Adaptive reward mechanism for sustainable online learning community. In AIED, pages 152–159.
d Baker, R. S., Corbett, A. T., Roll, I., and Koedinger, K. R. (2008). Developing a generalizable detector of when students game the system. User Modeling and User-Adapted Interaction, 18(3):287–314.
d Baker, R. S., Mitrović, A., and Mathews, M. (2010). Detecting gaming the system in constraint-based tutors. In International Conference on User Modeling, Adaptation, and Personalization, pages 267–278. Springer.
Friedman, J., Hastie, T., and Tibshirani, R. (2001). The elements of statistical learning. vol. 1 springer series in statistics. New York.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232.
Ghaleb, E., Popa, M., Hortal, E., Asteriadis, S., and Weiss, G. (2018). Towards affect recognition through interactions with learning materials. In 2018 17th IEEE international conference on machine learning and applications (ICMLA), pages 372–379. IEEE.
Gonçalves, L., Subtil, A., Oliveira, M. R., and de Zea Bermudez, P. (2014). Roc curve estimation: An overview. REVSTAT-Statistical journal, 12(1):1–20.
Gong, Y., Beck, J., Heffernan, N. T., and Forbes-Summers, E. (2010a). The impact of gaming (?) on learning at the fine-grained level. In Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part, volume 1, pages 194–203.
Gong, Y., Beck, J. E., Heffernan, N. T., and Forbes-Summers, E. (2010b). The finegrained impact of gaming (?) on learning. In International Conference on Intelligent Tutoring Systems, pages 194–203. Springer.
Hastie, T., Rosset, S., Zhu, J., and Zou, H. (2009). Multi-class adaboost. Statistics and its Interface, 2(3):349–360.
Johns, J. and Woolf, B. (2006). A dynamic mixture model to detect student motivation and proficiency. In AAAI, pages 163–168.
Liu, Y., Wang, Y., and Zhang, J. (2012). New machine learning algorithm: Random forest. In Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3, pages 246–252. Springer.
Mostow, J., Beck, J., Chalasani, R., Cuneo, A., Jia, P., Kadaru, K., et al. (2002). A la recherche du temps perdu, or as time goes by: Where does the time go in a reading tutor that listens? In International conference on intelligent tutoring systems, pages 320–329. Springer.
Paquette, L. and Baker, R. S. (2017). Variations of gaming behaviors across populations of students and across learning environments. In Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings 18, pages 274–286. Springer.
Paquette, L., de Carvalho, A. M., and Baker, R. S. (2014). Towards understanding expert coding of student disengagement in online learning. In CogSci.
Phua, C., Alahakoon, D., and Lee, V. (2004). Minority report in fraud detection: classification of skewed data. Acm sigkdd explorations newsletter, 6(1):50–59.
Richey, J. E., Zhang, J., Das, R., Andres-Bray, J. M., Scruggs, R., Mogessie, M., Baker, R. S., and McLaren, B. M. (2021). Gaming and confrustion explain learning advantages for a math digital learning game. In Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part I, pages 342–355. Springer.
Rocha, H. J. B., de Azevedo Restelli Tedesco, P. C., de Barros Costa, E., and Rocha, J. S. (2023). An approach for detecting gaming the system behavior in programming problem-solving. In International Conference on Intelligent Tutoring Systems, pages 75–87. Springer.
Yang, D., Sinha, T., Adamson, D., and Rosé, C. P. (2013). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS Data-driven education workshop, volume 11, page 14. Lake Tahoe, NV.
Aleven, V. and Koedinger, K. R. (2000). Limitations of student control: Do students know when they need help? In International conference on intelligent tutoring systems, pages 292–303. Springer.
Aleven, V., Mclaren, B., Roll, I., and Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16(2):101–128.
Baker, R. and de Carvalho, A. (2008). Labeling student behavior faster and more precisely with text replays. In Educational Data Mining 2008.
Baker, R. S., Corbett, A. T., and Koedinger, K. R. (2004a). Detecting student misuse of intelligent tutoring systems. In International conference on intelligent tutoring systems, pages 531–540. Springer.
Baker, R. S., Corbett, A. T., Koedinger, K. R., and Wagner, A. Z. (2004b). Off-task behavior in the cognitive tutor classroom: When students”game the system”. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 383–390.
Baker, R. S., Roll, I., Corbett, A. T., and Koedinger, K. R. (2005). Do performance goals lead students to game the system? In AIED, pages 57–64.
Beck, J. E. and Mostow, J. (2008). How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In International conference on intelligent tutoring systems, pages 353–362. Springer.
Brown, N. C. and Altadmri, A. (2017). Novice java programming mistakes: Largescale data vs. educator beliefs. ACM Transactions on Computing Education (TOCE), 17(2):1–21.
Cavalcanti, A. P., de Mello, R. F. L., de Miranda, P. B. C., and de Freitas, F. L. G. (2020). Análise automática de feedback em ambientes de aprendizagem online. In Anais do XXXI Simpósio Brasileiro de Informática na Educaçao, pages 892–901. SBC.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794.
Cheng, R. and Vassileva, J. (2005). Adaptive reward mechanism for sustainable online learning community. In AIED, pages 152–159.
d Baker, R. S., Corbett, A. T., Roll, I., and Koedinger, K. R. (2008). Developing a generalizable detector of when students game the system. User Modeling and User-Adapted Interaction, 18(3):287–314.
d Baker, R. S., Mitrović, A., and Mathews, M. (2010). Detecting gaming the system in constraint-based tutors. In International Conference on User Modeling, Adaptation, and Personalization, pages 267–278. Springer.
Friedman, J., Hastie, T., and Tibshirani, R. (2001). The elements of statistical learning. vol. 1 springer series in statistics. New York.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232.
Ghaleb, E., Popa, M., Hortal, E., Asteriadis, S., and Weiss, G. (2018). Towards affect recognition through interactions with learning materials. In 2018 17th IEEE international conference on machine learning and applications (ICMLA), pages 372–379. IEEE.
Gonçalves, L., Subtil, A., Oliveira, M. R., and de Zea Bermudez, P. (2014). Roc curve estimation: An overview. REVSTAT-Statistical journal, 12(1):1–20.
Gong, Y., Beck, J., Heffernan, N. T., and Forbes-Summers, E. (2010a). The impact of gaming (?) on learning at the fine-grained level. In Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS2010) Part, volume 1, pages 194–203.
Gong, Y., Beck, J. E., Heffernan, N. T., and Forbes-Summers, E. (2010b). The finegrained impact of gaming (?) on learning. In International Conference on Intelligent Tutoring Systems, pages 194–203. Springer.
Hastie, T., Rosset, S., Zhu, J., and Zou, H. (2009). Multi-class adaboost. Statistics and its Interface, 2(3):349–360.
Johns, J. and Woolf, B. (2006). A dynamic mixture model to detect student motivation and proficiency. In AAAI, pages 163–168.
Liu, Y., Wang, Y., and Zhang, J. (2012). New machine learning algorithm: Random forest. In Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3, pages 246–252. Springer.
Mostow, J., Beck, J., Chalasani, R., Cuneo, A., Jia, P., Kadaru, K., et al. (2002). A la recherche du temps perdu, or as time goes by: Where does the time go in a reading tutor that listens? In International conference on intelligent tutoring systems, pages 320–329. Springer.
Paquette, L. and Baker, R. S. (2017). Variations of gaming behaviors across populations of students and across learning environments. In Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings 18, pages 274–286. Springer.
Paquette, L., de Carvalho, A. M., and Baker, R. S. (2014). Towards understanding expert coding of student disengagement in online learning. In CogSci.
Phua, C., Alahakoon, D., and Lee, V. (2004). Minority report in fraud detection: classification of skewed data. Acm sigkdd explorations newsletter, 6(1):50–59.
Richey, J. E., Zhang, J., Das, R., Andres-Bray, J. M., Scruggs, R., Mogessie, M., Baker, R. S., and McLaren, B. M. (2021). Gaming and confrustion explain learning advantages for a math digital learning game. In Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part I, pages 342–355. Springer.
Rocha, H. J. B., de Azevedo Restelli Tedesco, P. C., de Barros Costa, E., and Rocha, J. S. (2023). An approach for detecting gaming the system behavior in programming problem-solving. In International Conference on Intelligent Tutoring Systems, pages 75–87. Springer.
Yang, D., Sinha, T., Adamson, D., and Rosé, C. P. (2013). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS Data-driven education workshop, volume 11, page 14. Lake Tahoe, NV.
Publicado
06/11/2023
Como Citar
ROCHA, Hemilis J. B.; COSTA, Evandro de B.; TEDESCO, Patricia C. de A. R..
Analisando a relação de conceitos de programação com o comportamento ”abuso do sistema” em programadores novatos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1716-1725.
DOI: https://doi.org/10.5753/sbie.2023.234821.
