O uso de estimativas de conhecimento do aluno em programação de computadores em modelos de detecção da emoção confusão livres de sensores
Resumo
Detectar a confusão do aluno permite ao ambiente computacional de aprendizagem realizar ações que ajudem o aluno a regular sua confusão e a se beneficiar dela. O artigo apresenta evidências sobre os efeitos de usar dados sobre estimativas de conhecimento do aluno, além de dados sobre a interação dele com o ambiente, no desempenho de modelos de detecção da confusão do aluno livres de sensores em tarefas de programação de computadores. Modelos de aprendizado de máquina foram treinados com amostras compostas por dados coletados de 62 alunos, durante cinco meses, em turmas de programação. Os resultados apresentaram evidências positivas que suportam a abordagem do estudo. O artigo também descreve cenários onde a abordagem é mais vantajosa.
Palavras-chave:
Programação de computadores, Confusão, Detecção de emoção, Modelo livres de sensores
Referências
Arguel, A., Lockyer, L., Kennedy, G., Lodge, J. M., and Pachman, M. (2019). Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments. Interactive Learning Environments, 27:200–210.
Arroyo, I., Cooper, D., Burleson, W., Woolf, B., Muldner, K., and Christopherson, R. (2009). Emotion sensors go to school. In Frontiers in Artificial Intelligence and Applications, number 1 in Frontiers in Artificial Intelligence and Applications, pages 17–24. IOS Press.
Badrinath, A., Wang, F., and Pardos, Z. (2021). pybkt: An accessible python library of bayesian knowledge tracing models. International Educational Data Mining Society.
Bosch, N., Chen, Y., and D’Mello, S. (2014). It’s written on your face: Detecting affective states from facial expressions while learning computer programming. In TrausanMatu, S., Boyer, K. E., Crosby, M., and Panourgia, K., editors, Intelligent Tutoring Systems, pages 39–44, Cham. Springer International Publishing.
Bosch, N. and D’Mello, S. (2017). The affective experience of novice computer programmers. International Journal of Artificial Intelligence in Education, 27(1):181–206.
Bosch, N., D’Mello, S., and Mills, C. (2013). What emotions do novices experience during their first computer programming learning session? In International Conference on Artificial Intelligence in Education, pages 11–20. Springer.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In André, E., Baker, R., Hu, X., Rodrigo, M. M. T., and du Boulay, B., editors, Artificial Intelligence in Education, pages 40–51, Cham. Springer International Publishing.
Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
Chen, T. and Guestrin, C. (2016). Xgboost. volume 42, pages 785–794. ACM.
Chi, M. T. and Ohlsson, S. (2005). Complex Declarative Learning. Cambridge University Press.
Coto, M., Mora, S., Grass, B., and Murillo-Morera, J. (2021). Emotions and programming learning: systematic mapping. Computer Science Education, pages 1–36.
D’Mello, S., Person, N., and Lehman, B. (2009). Antecedent-consequent relationships and cyclical patterns between affective states and problem solving outcomes. Frontiers in Artificial Intelligence and Applications, 200:57–64.
D’Mello, S. K. (2020). Big data in the science of learning. In Big data in psychological research., pages 203–225. American Psychological Association.
D’Mello, S., Lehman, B., Pekrun, R., and Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29:153 – 170.
D’Mello, S. K. and Graesser, A. C. (2014). Confusion. In International handbook of emotions in education, pages 299–320. Routledge.
Felipe, D. A. M., Gutierrez, K. I. N., Quiros, E. C. M., and Vea, L. A. (2012). Towards the development of intelligent agent for novice c/c++ programmers through affective analysis of event logs. In Proc. Int. MultiConference Eng. Comput. Sci, volume 1, page 2012. Citeseer.
Graesser, A., Chipman, P., King, B., McDaniel, B., and D’Mello, S. (2007). Emotions and learning with autotutor. In Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, page 569–571, NLD. IOS Press.
Grafsgaard, J. F., Boyer, K. E., and Lester, J. C. (2011). Predicting facial indicators of confusion with hidden markov models. In D’Mello, S., Graesser, A., Schuller, B., and Martin, J.-C., editors, Affective Computing and Intelligent Interaction, pages 97–106, Berlin, Heidelberg. Springer Berlin Heidelberg.
Kubat, M. (2017). An introduction to machine learning, volume 2. Springer.
Kölling, M., Quig, B., Patterson, A., and Rosenberg, J. (2003). The bluej system and its pedagogy. Computer Science Education, 13:249–268.
Lee, D. M. C., Rodrigo, M. M. T., d Baker, R. S., Sugay, J. O., and Coronel, A. (2011). Exploring the relationship between novice programmer confusion and achievement. In International Conference on Affective Computing and Intelligent Interaction, pages 175–184. Springer.
Lehman, B., D’Mello, S., Strain, A., Mills, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., and Graesser, A. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education, 22:85–105.
Ocumpaugh, J. (2015). Baker rodrigo ocumpaugh monitoring protocol (bromp) 2.0 technical and training manual. New York, NY and Manila, Philippines: Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences.
Paper, D. (2021). Introduction to Deep Learning, pages 1–24. Apress, Berkeley, CA.
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In New perspectives on affect and learning technologies, pages 23–39. Springer.
Picard, R. W. (2010). Affective computing: From laughter to ieee. IEEE Transactions on Affective Computing, 1:11–17.
Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning.
Rodrigo, M. M. T., Baker, R. S. J., and Nabos, J. Q. (2010). The relationships between sequences of affective states and learner achievement. Proceedings of the 18th International Conference on Computers in Education, pages 56–60.
Scherer, K. R. (2005). What are emotions? and how can they be measured? Social science information, 44(4):695–729.
Shwartz-Ziv, R. and Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81:84–90.
Silvia, P. J. (2010). Confusion and interest: The role of knowledge emotions in aesthetic experience. Psychology of Aesthetics, Creativity, and the Arts, 4:75–80.
Thi, H. A. L., Nguyen, V. V., and Ouchani, S. (2008). Gene selection for cancer classification using dca. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5139 LNAI:62–72.
Tiam-Lee, T. J. and Sumi, K. (2018). Adaptive feedback based on student emotion in a system for programming practice. In Nkambou, R., Azevedo, R., and Vassileva, J., editors, Intelligent Tutoring Systems, pages 243–255, Cham. Springer International Publishing.
Tiam-Lee, T. J. and Sumi, K. (2019). Analysis and prediction of student emotions while doing programming exercises. In International conference on intelligent tutoring systems, pages 24–33. Springer.
Vea, L. and Rodrigo, M. M. (2017). Modeling negative affect detector of novice programming students using keyboard dynamics and mouse behavior. In Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., and Pramkeaw, P., editors, Trends in Artificial Intelligence: PRICAI 2016 Workshops, pages 127–138, Cham. Springer International Publishing.
Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48:2839–2846.
Yang, T.-Y., Baker, R. S., Studer, C., Heffernan, N., and Lan, A. S. (2019). Active learning for student affect detection. International Educational Data Mining Society.
Arroyo, I., Cooper, D., Burleson, W., Woolf, B., Muldner, K., and Christopherson, R. (2009). Emotion sensors go to school. In Frontiers in Artificial Intelligence and Applications, number 1 in Frontiers in Artificial Intelligence and Applications, pages 17–24. IOS Press.
Badrinath, A., Wang, F., and Pardos, Z. (2021). pybkt: An accessible python library of bayesian knowledge tracing models. International Educational Data Mining Society.
Bosch, N., Chen, Y., and D’Mello, S. (2014). It’s written on your face: Detecting affective states from facial expressions while learning computer programming. In TrausanMatu, S., Boyer, K. E., Crosby, M., and Panourgia, K., editors, Intelligent Tutoring Systems, pages 39–44, Cham. Springer International Publishing.
Bosch, N. and D’Mello, S. (2017). The affective experience of novice computer programmers. International Journal of Artificial Intelligence in Education, 27(1):181–206.
Bosch, N., D’Mello, S., and Mills, C. (2013). What emotions do novices experience during their first computer programming learning session? In International Conference on Artificial Intelligence in Education, pages 11–20. Springer.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In André, E., Baker, R., Hu, X., Rodrigo, M. M. T., and du Boulay, B., editors, Artificial Intelligence in Education, pages 40–51, Cham. Springer International Publishing.
Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
Chen, T. and Guestrin, C. (2016). Xgboost. volume 42, pages 785–794. ACM.
Chi, M. T. and Ohlsson, S. (2005). Complex Declarative Learning. Cambridge University Press.
Coto, M., Mora, S., Grass, B., and Murillo-Morera, J. (2021). Emotions and programming learning: systematic mapping. Computer Science Education, pages 1–36.
D’Mello, S., Person, N., and Lehman, B. (2009). Antecedent-consequent relationships and cyclical patterns between affective states and problem solving outcomes. Frontiers in Artificial Intelligence and Applications, 200:57–64.
D’Mello, S. K. (2020). Big data in the science of learning. In Big data in psychological research., pages 203–225. American Psychological Association.
D’Mello, S., Lehman, B., Pekrun, R., and Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29:153 – 170.
D’Mello, S. K. and Graesser, A. C. (2014). Confusion. In International handbook of emotions in education, pages 299–320. Routledge.
Felipe, D. A. M., Gutierrez, K. I. N., Quiros, E. C. M., and Vea, L. A. (2012). Towards the development of intelligent agent for novice c/c++ programmers through affective analysis of event logs. In Proc. Int. MultiConference Eng. Comput. Sci, volume 1, page 2012. Citeseer.
Graesser, A., Chipman, P., King, B., McDaniel, B., and D’Mello, S. (2007). Emotions and learning with autotutor. In Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, page 569–571, NLD. IOS Press.
Grafsgaard, J. F., Boyer, K. E., and Lester, J. C. (2011). Predicting facial indicators of confusion with hidden markov models. In D’Mello, S., Graesser, A., Schuller, B., and Martin, J.-C., editors, Affective Computing and Intelligent Interaction, pages 97–106, Berlin, Heidelberg. Springer Berlin Heidelberg.
Kubat, M. (2017). An introduction to machine learning, volume 2. Springer.
Kölling, M., Quig, B., Patterson, A., and Rosenberg, J. (2003). The bluej system and its pedagogy. Computer Science Education, 13:249–268.
Lee, D. M. C., Rodrigo, M. M. T., d Baker, R. S., Sugay, J. O., and Coronel, A. (2011). Exploring the relationship between novice programmer confusion and achievement. In International Conference on Affective Computing and Intelligent Interaction, pages 175–184. Springer.
Lehman, B., D’Mello, S., Strain, A., Mills, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., and Graesser, A. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education, 22:85–105.
Ocumpaugh, J. (2015). Baker rodrigo ocumpaugh monitoring protocol (bromp) 2.0 technical and training manual. New York, NY and Manila, Philippines: Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences.
Paper, D. (2021). Introduction to Deep Learning, pages 1–24. Apress, Berkeley, CA.
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In New perspectives on affect and learning technologies, pages 23–39. Springer.
Picard, R. W. (2010). Affective computing: From laughter to ieee. IEEE Transactions on Affective Computing, 1:11–17.
Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning.
Rodrigo, M. M. T., Baker, R. S. J., and Nabos, J. Q. (2010). The relationships between sequences of affective states and learner achievement. Proceedings of the 18th International Conference on Computers in Education, pages 56–60.
Scherer, K. R. (2005). What are emotions? and how can they be measured? Social science information, 44(4):695–729.
Shwartz-Ziv, R. and Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81:84–90.
Silvia, P. J. (2010). Confusion and interest: The role of knowledge emotions in aesthetic experience. Psychology of Aesthetics, Creativity, and the Arts, 4:75–80.
Thi, H. A. L., Nguyen, V. V., and Ouchani, S. (2008). Gene selection for cancer classification using dca. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5139 LNAI:62–72.
Tiam-Lee, T. J. and Sumi, K. (2018). Adaptive feedback based on student emotion in a system for programming practice. In Nkambou, R., Azevedo, R., and Vassileva, J., editors, Intelligent Tutoring Systems, pages 243–255, Cham. Springer International Publishing.
Tiam-Lee, T. J. and Sumi, K. (2019). Analysis and prediction of student emotions while doing programming exercises. In International conference on intelligent tutoring systems, pages 24–33. Springer.
Vea, L. and Rodrigo, M. M. (2017). Modeling negative affect detector of novice programming students using keyboard dynamics and mouse behavior. In Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., and Pramkeaw, P., editors, Trends in Artificial Intelligence: PRICAI 2016 Workshops, pages 127–138, Cham. Springer International Publishing.
Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48:2839–2846.
Yang, T.-Y., Baker, R. S., Studer, C., Heffernan, N., and Lan, A. S. (2019). Active learning for student affect detection. International Educational Data Mining Society.
Publicado
16/11/2022
Como Citar
KAUTZMANN, Tiago R.; RAMOS, Gabriel de O.; JAQUES, Patrícia A..
O uso de estimativas de conhecimento do aluno em programação de computadores em modelos de detecção da emoção confusão livres de sensores. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 33. , 2022, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 1196-1208.
DOI: https://doi.org/10.5753/sbie.2022.225768.
