Tutor Inteligente con reconocimiento y manejo de emociones para Matemáticas
Palabras clave:
Instrucción Basada en Web, Sistemas Tutores Inteligentes, Aprendizaje Mediado por Computadora, Computación AfectivaResumen
En el presente trabajo se describe el desarrollo, implementación y pruebas de un Sistema Tutor Inteligente para matemáticas de tercer grado de primaria que identifica el estado emocional del estudiante y produce retroalimentación afectiva para el mismo durante un curso, el cual se encuentra instalado en una red social. El reconocimiento de emociones se lleva a cabo a través de expresiones faciales, lo cual se realiza por medio de una red neuronal artificial. La red social y el Sistema Tutor Inteligente con manejo afectivo han sido probados en escuelas públicas y privadas de la localidad, con resultados muy favorables.Descargas
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Referencias
Anderson, R., Boyle, C. F., Corbett, A. T. y Lewis, M. W. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42, 17-49.
Arroyo, I., Woolf, B., Cooper, D., Burleson, W., Muldner, K. y Christopherson, R. (2009). Emotions sensors go to school. In V. Diminitrova, R., Mizoguchi, B. Du Boulay y A. Graesser (Eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 17-24). Amsterdam: IOS Press.
Conati, C. y McLaren, H. (2004). Evaluating a probabilistic model of student affect. Proceedings of ITS 2004. Documento presentado en la 7a. Conferencia Internacional de Sistemas Tutoriales Inteligentes (vol. 3220). Heidelberg: Springer.
Davidson, R. J., Scherer, K. R. y Goldsmith, H. H. (Eds.). (2003). Handbook of affective sciences. Oxford University Press.
D’Mello, S. K., Picard, R. W. y Graesser, A. C. (2007). Towards an affective-sensitive autotutor. Special issue on Intelligent Educational Systems. IEEE Intelligent Systems, 22(4), 53-61.
D’Mello, S. K., Dowell, N. y Graesser, A. C. (2009). Cohesion relationships in tutorial dialogue as predictors of affective states. En V. Dimitrova, R. Mizoguchi, B. du Boulay, y A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education (pp. 9-16). Amsterdam: IOS Press.
D’Mello, S., Lehman, B. y Graesser, A. C. (2011). A motivationally supportive affect-sensitive autotutor. New perspectives on affect and learning technologies, explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 113-126, Springer science+business media.
Dong, A. (2011). The role of affect in creative minds. New perspectives on affect and learning technologies, Explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 217-232, Springer science+business media.
Du Boulay, B. (2011). Towards a motivationally intelligence pedagogy: how should an intelligent tutor respond to the unmotivated or the demotivated?. New perspectives on affect and learning technologies, Explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 41-52, Springer science+business media.
Ekman, P. (1999). Facial Expressions. Nueva York: John Wiley & Sons Ltd.
Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56, 218-226.
Forbes-Riley, K., Litman, D. J. (2009). Adapting to student uncertainty improves tutoring dialogues. Brighton, Inglaterra: IOS Press.
Hernández, Y., Sucar, L. E., Arroyo G. (2008). Building an affective model for intelligent tutoring systems with base on teachers’ expertise, pp 754-764, Springer-verlag.
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D., Hawk, S. y van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. Cognition & Emotion, 1377-1388.
Lepper, M. R. and Hodell, M. (1989). Intrinsic motivation in the classroom. En C. Ames y R. E. Ames (Eds.) Research in motivation in education (pp. 73-105). Nueva York: Academic.
Loewenstein, G. y Lerner, J.S. (2003). The role of affect in decision making. En R. J. Davidson, K. R. Scherer, H. Hill Goldsmith (Eds.), Handbook of affective sciences (pp. 619-642). Oxford University Press.
Nugent, P. M. (2007). Lattice multiplication in a preservice classroom. Mathematics Teaching in the Middle School, 13(2), 110-113.
Picard, R. W. (1995). Affective computing. Reporte técnico 321. Massachusetts Institute of Technology.
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Rrsnick, M., Roy, D. y Strohecker, C. (2004). Affective Learning-A Manifesto. BT Technical Journal, 2(4), 253-269.
Scherer, K. R. (2009). The dynamic architecture of emotions: Evidence for the component process model. Cognition & Emotion, 23, 1307-1351.
Van de Walle, J. A. y Lovin, L. H. (2006). Teaching Student-centered Mathematics, grades 5-8. Saddle River, NJ: Pearson Education.
Vygotsky, L. S. (1978). Mind in Society: The development of higher psychological process. Cambridge, MA. Harvard University Press,
Arroyo, I., Woolf, B., Cooper, D., Burleson, W., Muldner, K. y Christopherson, R. (2009). Emotions sensors go to school. In V. Diminitrova, R., Mizoguchi, B. Du Boulay y A. Graesser (Eds.) Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 17-24). Amsterdam: IOS Press.
Conati, C. y McLaren, H. (2004). Evaluating a probabilistic model of student affect. Proceedings of ITS 2004. Documento presentado en la 7a. Conferencia Internacional de Sistemas Tutoriales Inteligentes (vol. 3220). Heidelberg: Springer.
Davidson, R. J., Scherer, K. R. y Goldsmith, H. H. (Eds.). (2003). Handbook of affective sciences. Oxford University Press.
D’Mello, S. K., Picard, R. W. y Graesser, A. C. (2007). Towards an affective-sensitive autotutor. Special issue on Intelligent Educational Systems. IEEE Intelligent Systems, 22(4), 53-61.
D’Mello, S. K., Dowell, N. y Graesser, A. C. (2009). Cohesion relationships in tutorial dialogue as predictors of affective states. En V. Dimitrova, R. Mizoguchi, B. du Boulay, y A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education (pp. 9-16). Amsterdam: IOS Press.
D’Mello, S., Lehman, B. y Graesser, A. C. (2011). A motivationally supportive affect-sensitive autotutor. New perspectives on affect and learning technologies, explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 113-126, Springer science+business media.
Dong, A. (2011). The role of affect in creative minds. New perspectives on affect and learning technologies, Explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 217-232, Springer science+business media.
Du Boulay, B. (2011). Towards a motivationally intelligence pedagogy: how should an intelligent tutor respond to the unmotivated or the demotivated?. New perspectives on affect and learning technologies, Explorations in the learning sciences, instructional systems and performance technologies, 3, pp. 41-52, Springer science+business media.
Ekman, P. (1999). Facial Expressions. Nueva York: John Wiley & Sons Ltd.
Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56, 218-226.
Forbes-Riley, K., Litman, D. J. (2009). Adapting to student uncertainty improves tutoring dialogues. Brighton, Inglaterra: IOS Press.
Hernández, Y., Sucar, L. E., Arroyo G. (2008). Building an affective model for intelligent tutoring systems with base on teachers’ expertise, pp 754-764, Springer-verlag.
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D., Hawk, S. y van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. Cognition & Emotion, 1377-1388.
Lepper, M. R. and Hodell, M. (1989). Intrinsic motivation in the classroom. En C. Ames y R. E. Ames (Eds.) Research in motivation in education (pp. 73-105). Nueva York: Academic.
Loewenstein, G. y Lerner, J.S. (2003). The role of affect in decision making. En R. J. Davidson, K. R. Scherer, H. Hill Goldsmith (Eds.), Handbook of affective sciences (pp. 619-642). Oxford University Press.
Nugent, P. M. (2007). Lattice multiplication in a preservice classroom. Mathematics Teaching in the Middle School, 13(2), 110-113.
Picard, R. W. (1995). Affective computing. Reporte técnico 321. Massachusetts Institute of Technology.
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Rrsnick, M., Roy, D. y Strohecker, C. (2004). Affective Learning-A Manifesto. BT Technical Journal, 2(4), 253-269.
Scherer, K. R. (2009). The dynamic architecture of emotions: Evidence for the component process model. Cognition & Emotion, 23, 1307-1351.
Van de Walle, J. A. y Lovin, L. H. (2006). Teaching Student-centered Mathematics, grades 5-8. Saddle River, NJ: Pearson Education.
Vygotsky, L. S. (1978). Mind in Society: The development of higher psychological process. Cambridge, MA. Harvard University Press,
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2014-10-27
