Reconhecimento e Adaptação à Dinâmica de Estados Afetivos Relacionados à Aprendizagem
Resumo
Reconhecer e responder adequadamente às reações afetivas dos aprendizes tem emergido como uma funcionalidade fundamental para a construção de uma nova geração ambientes computacionais de aprendizagem adaptativos. Neste trabalho é apresentado um modelo híbrido de inferência de estados afetivos relacionados à aprendizagem com baixa intrusividade. Este modelo permite a obtenção de informações que indicam situações relevantes para o aprendizado, como “ciclo vicioso” e “concentração engajada”. Resultados promissores obtidos em um experimento com estudantes indicam a viabilidade desta proposta e também embasam a apresentação de alternativas de adaptação ou implementação de intervenções pedagógicas personalizadas.
Palavras-chave:
afetivos, aprendizagem, ambientes adaptativos, inferência de estados afetivos, intervenções pedagógicas
Referências
Baker, R., D'Mello, S., Rodrigo, M., and Graesser, A. (2010). Better to be frustrated than bored: The incidence and persistence of affect during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4):223-241.
Baker, R. S., Gowda, S., Wixon, M., Kalka, J., Wagner, A., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., and Rossi, L. (2012). Sensor-free automated detection of affect in a cognitive tutor for algebra. In Educational Data Mining 2012.
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 International Conference on Intelligent Tutoring Systems, pages 39-44. Springer.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In International Conference on Artificial Intelligence in Education, pages 40-51. Springer.
Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1):49-59.
Conati, C. (2011). Combining cognitive appraisal and sensors for affect detection in a framework for modeling user affect. In New Perspectives on Affect and Learning Technologies, pages 71-84. Springer.
D'Mello, S., Picard, R. W., and Graesser, A. (2007). Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4).
D'Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., and Graesser, A. (2010). A time for emoting: When affect-sensitivity is and isn't effective at promoting deep learning. In Intelligent Tutoring Systems, pages 245-254. Springer.
Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4):169-200.
Gottardo, E. and Pimentel, A. (2017). Modelo híbrido de inferência de emoções para ambientes de aprendizagem: uma proposta baseada na fusão de componentes físicos e cognitivos. In Brazilian Symposium on Computers in Education (Simpsío Brasileiro de Informática na Educação - SBIE), volume 28, pages 1778-1780.
Gottardo, E. and Pimentel, A. R. (2018). Improving inference of learning related emotion by combining cognitive and physical information. In International Conference on Intelligent Tutoring Systems, pages 313-318. Springer.
Hayashi, E., Posada, J. E. G., Maike, V. R., and Baranauskas, M. C. C. (2016). Exploring new formats of the self-assessment manikin in the design with children. In Proceedings of the 15th Brazilian Symposium on Human Factors in Computer Systems, page 27. ACM.
Jaques, N., Conati, C., Harley, J. M., and Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system. In International Conference on Intelligent Tutoring Systems, pages 29-38. Springer.
Kort, B., Reilly, R., and Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy—building a learning companion. In Advanced Learning Technologies, 2001. Proceedings. IEEE International Conference on, pages 43-46. IEEE.
Ortony, A., Clore, G. L., and Collins, A. (1990). The cognitive structure of emotions. Cambridge University Press.
Paquette, L., Baker, R. S., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., and Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. In International Conference on Intelligent Tutoring Systems, pages 1-10. Springer.
Picard, R. W. (1997). Affective Computing, volume 252. MIT Press, Cambridge.
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., and Strohecker, C. (2004). Affective learning—a manifesto. BT Technology Journal, 22(4):253-269.
Reis, H., Alvares, D., Jaques, P., and Isotani, S. (2018). Analysis of permanence time in emotional states: A case study using educational software. In International Conference on Intelligent Tutoring Systems - ITS, pages 180-190. Springer.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39:1161-1178.
Shen, L., Wang, M., and Shen, R. (2009). Affective e-learning: Using "emotional" data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12(2):176.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., and Picard, R. (2009). Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3-4):129-164.
Baker, R. S., Gowda, S., Wixon, M., Kalka, J., Wagner, A., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., and Rossi, L. (2012). Sensor-free automated detection of affect in a cognitive tutor for algebra. In Educational Data Mining 2012.
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 International Conference on Intelligent Tutoring Systems, pages 39-44. Springer.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In International Conference on Artificial Intelligence in Education, pages 40-51. Springer.
Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1):49-59.
Conati, C. (2011). Combining cognitive appraisal and sensors for affect detection in a framework for modeling user affect. In New Perspectives on Affect and Learning Technologies, pages 71-84. Springer.
D'Mello, S., Picard, R. W., and Graesser, A. (2007). Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4).
D'Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., and Graesser, A. (2010). A time for emoting: When affect-sensitivity is and isn't effective at promoting deep learning. In Intelligent Tutoring Systems, pages 245-254. Springer.
Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4):169-200.
Gottardo, E. and Pimentel, A. (2017). Modelo híbrido de inferência de emoções para ambientes de aprendizagem: uma proposta baseada na fusão de componentes físicos e cognitivos. In Brazilian Symposium on Computers in Education (Simpsío Brasileiro de Informática na Educação - SBIE), volume 28, pages 1778-1780.
Gottardo, E. and Pimentel, A. R. (2018). Improving inference of learning related emotion by combining cognitive and physical information. In International Conference on Intelligent Tutoring Systems, pages 313-318. Springer.
Hayashi, E., Posada, J. E. G., Maike, V. R., and Baranauskas, M. C. C. (2016). Exploring new formats of the self-assessment manikin in the design with children. In Proceedings of the 15th Brazilian Symposium on Human Factors in Computer Systems, page 27. ACM.
Jaques, N., Conati, C., Harley, J. M., and Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system. In International Conference on Intelligent Tutoring Systems, pages 29-38. Springer.
Kort, B., Reilly, R., and Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy—building a learning companion. In Advanced Learning Technologies, 2001. Proceedings. IEEE International Conference on, pages 43-46. IEEE.
Ortony, A., Clore, G. L., and Collins, A. (1990). The cognitive structure of emotions. Cambridge University Press.
Paquette, L., Baker, R. S., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., and Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. In International Conference on Intelligent Tutoring Systems, pages 1-10. Springer.
Picard, R. W. (1997). Affective Computing, volume 252. MIT Press, Cambridge.
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., and Strohecker, C. (2004). Affective learning—a manifesto. BT Technology Journal, 22(4):253-269.
Reis, H., Alvares, D., Jaques, P., and Isotani, S. (2018). Analysis of permanence time in emotional states: A case study using educational software. In International Conference on Intelligent Tutoring Systems - ITS, pages 180-190. Springer.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39:1161-1178.
Shen, L., Wang, M., and Shen, R. (2009). Affective e-learning: Using "emotional" data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12(2):176.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., and Picard, R. (2009). Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3-4):129-164.
Publicado
29/10/2018
Como Citar
GOTTARDO, Ernani; PIMENTEL, Andrey Ricardo.
Reconhecimento e Adaptação à Dinâmica de Estados Afetivos Relacionados à Aprendizagem. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1223-1232.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1223.
