Emociones en el Aprendizaje: Estimando la Duración de la Confusión y Mejorando las Intervenciones Pedagógicas
DOI:
https://doi.org/10.5753/rbie.2023.3433Keywords:
Emociones académicas, Personalidad, Duración de emociones académicas, Frustración, Tiempo de permanencia, Análisis de supervivencia, Sistemas tutores inteligentes, Entornos de aprendizaje inteligentesAbstract
Este artículo presenta un modelo basado en el análisis de supervivencia para estimar la duración de la emoción de confusión en estudiantes durante el aprendizaje. La confusión académica puede tener efectos tanto positivos como negativos, y su persistencia puede llevar a emociones negativas. El modelo considera factores cruciales como los rasgos de personalidad y el conocimiento previo de los alumnos, los cuales han demostrado influir significativamente en la duración de la confusión. Para investigar esta relación, se recopilaron datos de estudiantes de séptimo grado que utilizaron un sistema de tutoría inteligente para resolver problemas de álgebra. Los resultados del análisis de los datos de 25 estudiantes revelaron diferencias estadísticamente significativas en la duración de la confusión según los diferentes rasgos de personalidad y el conocimiento previo de álgebra. También se propuso un modelo de intervención para momentos de confusión en entornos inteligentes de aprendizaje, basado en el modelo desarrollado. Este módulo determina el mejor momento para intervenir y brindar asistencia personalizada al conocimiento del alumno para el problema en cuestión. El estudio contribuye a la comprensión de la dinámica de la confusión académica y resalta la importancia de considerar las emociones y su duración, así como los rasgos de personalidad y el conocimiento previo de los alumnos, al diseñar intervenciones adecuadas en entornos inteligentes de aprendizaje. Identificar el momento oportuno para intervenir cuando un alumno está confundido es esencial para promover un proceso de aprendizaje más efectivo, permitiendo que los educadores adopten enfoques personalizados para satisfacer las necesidades individuales de los estudiantes y facilitar su proceso de aprendizaje.
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Baker, R., D’Mello, S., Rodrigo, M., & Graesser, A. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
Barbalet, J. (2002). Introduction: Why Emotions are Crucial. The Sociological Review, 50, 1–9. https://doi.org/10.1111/j.1467-954X.2002.tb03588.x
Barbosa, A. (2009). Modelo hierárquico de fobias infanto-juvenis: Testagem e relação com os estilos maternos (tese de dout.) [GS Search]. Universidade Federal do Rio Grande do Norte.
Chaouachi, M., & Frasson, C. (2012). Mental workload, engagement and emotions: an exploratory study for intelligent tutoring systems. International Conference on Intelligent Tutoring Systems, 65–71. https://doi.org/10.1007/978-3-642-30950-2_9
Ciorbea, I., & Pasarica, F. (2013). The study of the relationship between personality and academic performance. Procedia-Social and Behavioral Sciences, 78, 400–404. https://doi.org/10.1016/j.sbspro.2013.04.319
Colosimo, E. A., & Giolo, S. R. (2006). Análise de sobrevivência aplicada (First). Editora Blucher. [GS Search]
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. http://doi.org/10.1007/978-1-4612-43809_37
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of educational media, 29(3), 241–250. https://doi.org/10.1080/1358165042000283101
Csikszentmihalyi, M. (2000). Beyond boredom and anxiety (First) . [GS Search] Jossey-Bass.
De Feyter, T., Caers, R., Vigna, C., & Berings, D. (2012). Unraveling the impact of the Big Five personality traits on academic performance: The moderating and mediating effects of self-efficacy and academic motivation. Learning and Individual Differences, 22(4), 439–448. https://doi.org/10.1016/j.lindif.2012.03.013
Di Leo, I., Muis, K. R., Singh, C. A., & Psaradellis, C. (2019). Curiosity... Confusion? Frustration! The role and sequencing of emotions during mathematics problem solving. Contemporary educational psychology, 58, 121–137. https://doi.org/10.1016/j.cedpsych.2019.03.001
D’Mello, S., & Calvo, R. (2013). Beyond the basic emotions: what should affective computing compute? Extended Abstracts on Human Factors in Computing Systems, 2287–2294. https://doi.org/10.1145/2468356.2468751
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003
D’Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of human-computer studies, 70(5), 377–398. https://doi.org/10.1016/j.ijhcs.2012.01.004
D’Mello, S., Picard, R. W., & Graesser, A. (2007). Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems, 22(4), 53–61. https://doi.org/10.1109/mis.2007.79
D’Mello, S., Taylor, R., & Graesser, A. (2012). Monitoring Affective Trajectories During Complex Learning. Encyclopedia of the Sciences of Learning, 203–208. https://doi.org/10.1007/978-1-4419-1428-6_849
Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development (First) . Psychology Press.
Eagle, M., & Barnes, T. (2014). Survival analysis on duration data in intelligent tutors. International Conference on Intelligent Tutoring Systems, 178–187. https://doi.org/10.1007/978-3-319-07221-0_22
Eysenck, H. J. (2017). The Structure of Personality (First). Routledge. https://doi.org/10.4324/9781351305280-2
Feltz, A., & Cokely, E. T. (2012). The philosophical personality argument . [GS Search]. Philosophical Studies, 161(2), 227–246.
Frijda, N. (1986). The Emotions (First). Cambridge University Press. https://doi.org/10.4135/9781848608399.n12
Goldberg, L. R. (1981). Language and individual differences: The search for universals in personality lexicons . [GS Search]. Review of personality and social psychology, 2(1), 141–165.
Graesser, A., & D’Mello, S. (2011). Theoretical Perspectives on Affect and Deep Learning (First). Springer New York. https://doi.org/10.1007/978-1-4419-9625-1_2
Graesser, A., & Olde, B. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524. https://doi.org/10.1037/0022-0663.95.3.524
Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., & Popovich, D. L. (2015). Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology, 25(4), 652–665. https://doi.org/10.2139/ssrn.2663427
Jaques, P. A., Seffrin, H., Rubi, G., de Morais, F., Ghilardi, C., Bittencourt, I. I., & Isotani, S. (2013). Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2Math. Expert Systems with Applications, 40(14), 5456–5465. https://doi.org/10.1016/j.eswa.2013.04.004
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American statistical association, 53(282), 457–481. http://doi.org/10.1007/9781-4612-4380-9_25
Kassambara, A. (2016). Cox Proportional Hazards Model in R [Accessed on: December 28, 2021]. [Link]
Kautzmann, T. R. (2014). Um Modelo de agente metacognitivo para o treinamento da habilidade de monitoramento do conhecimento: um estudo de caso com o sistema tutor inteligente PAT2Math (diss. de mestr.). Universidade do Vale do Rio dos Sinos. [Link]
Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. Proceedings IEEE International Conference on Advanced Learning Technologies, 43–46. https://doi.org/10.1109/ICALT.2001.943850
Malekzadeh, M., Mustafa, M. B., & Lahsasna, A. (2015). A review of emotion regulation in intelligent tutoring systems. Journal of Educational Technology & Society, 18(4), 435–445. https://www.jstor.org/stable/jeductechsoci.18.4.435
McCrae, R. R., & Costa, P. T. (1983). Social desirability scales: More substance than style. Journal of consulting and clinical psychology, 51(6), 882–888. https://doi.org/10.1037/0022-006x.51.6.882
McQuiggan, S. W., Lee, S., & Lester, J. C. (2007). Early prediction of student frustration. International Conference on Affective Computing and Intelligent Interaction, 698–709. https://doi.org/10.1007/978-3-540-74889-2_61
Morais, F. d. (2018). Detecção e predição de estados afetivos baseados em mineração de dados educacionais: Considerando a personalidade do aluno para aumentar a precisão da detecção (diss. de mestr.). Universidade do Vale do Rio dos Sinos. https://doi.org/10.5753/cbie.wcbie.2019.1052
Muramatsu, K., Tanaka, E., Watanuki, K., & Matsui, T. (2016). Framework to describe constructs of academic emotions using ontological descriptions of statistical models. Research and practice in technology enhanced learning, 11(1), 1–18. https://doi.org/10.1186/s41039-016-0029-1
Murphy, L., Eduljee, N. B., Croteau, K., & Parkman, S. (2020). Relationship between Personality Type and Preferred Teaching Methods for Undergraduate College Students. International Journal of Research in Education and Science, 6(1), 100–109. https://eric.ed.gov/?id=EJ1229010
Nießen, D., Danner, D., Spengler, M., & Lechner, C. M. (2020). Big Five personality traits predict successful transitions from school to vocational education and training: a large-scale study. Frontiers in psychology, 1–18. https://doi.org/10.3389/fpsyg.2020.01827
Nussbaum, E. M. (2002). How introverts versus extroverts approach small-group argumentative discussions. The Elementary School Journal, 102(3), 183–197. https://doi.org/10.1086/499699
Nussbaum, E. M., & Bendixen, L. D. (2003). Approaching and avoiding arguments: The role of epistemological beliefs, need for cognition, and extraverted personality traits. Contemporary Educational Psychology, 28(4), 573–595. https://doi.org/10.1016/s0361-476x(02)00062-0
Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions. Cambridge university press. https://doi.org/10.1017/9781108934053
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R. (2014). Emotions and learning. Educational practices series, 24(1), 1–31. [Link]
Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: Exploring control–value antecedents and performance outcomes of a neglected emotion. Journal of educational psychology, 102(3). https://doi.org/10.1037/a0019243
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91–105. https://doi.org/10.4324/9781410608628-4
Piaget, J., & Cook, M. T. (1952). The Origins of Intelligence in Children (First). WW Norton; Co. https://doi.org/10.1037/11494-000
Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128–140. https://doi.org/10.1016/j.learninstruc.2013.02.006
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin, 135(2). https://doi.org/10.1037/a0014996
Poropat, A. E. (2011). The Eysenckian personality factors and their correlations with academic performance. British Journal of Educational Psychology, 81(1), 41–58. https://doi.org/10.1348/000709910x497671
Raad, B., & Schouwenburg, H. (1996). Personality in learning and education: a review. European Journal of Personality, 10, 303–336. https://doi.org/10.1002/(sici)1099-0984(199612)10:5<303::aid-per262>3.0.co;2-2
Rammstedt, B., Danner, D., & Lechner, C. (2017). Personality, competencies, and life outcomes: results from the German PIAAC longitudinal study. Large-scale assessments in education, 5(1), 1–19. https://doi.org/10.1186/s40536-017-0035-9
Reis, H., Alvares, D., Isotani, S., & Jaques, P. A. (2021). Customização da Regulação Emocional de acordo com a Personalidade dos Estudantes em Sistemas Tutores Inteligentes. Revista Brasileira de Informática na Educação (RBIE), 29, 48–72. https://doi.org/10.5753/rbie.2021.29.0.48
Reis, H., Alvares, D., Jaques, P., & Isotani, S. (2018). Analysis of permanence time in emotional states: A case study using educational software. International conference on intelligent tutoring systems, 180–190. https://doi.org/10.1007/978-3-319-91464-0_18
Reis, H., Jaques, P. A., & Isotani, S. (2017). Sistemas Tutores Inteligentes que reconhecem o estado emocional do estudante: Um mapeamento sistemático. Research and Innovation in Brazilian Education, 101–114.
Saccaro, A., França, M. T. A., & Jacinto, P. d. A. (2019). Fatores Associados à Evasão no Ensino Superior Brasileiro: um estudo de análise de sobrevivência para os cursos das áreas de Ciência, Matemática e Computação e de Engenharia, Produção e Construção em instituições públicas e privadas. Estudos Econômicos (São Paulo), 49, 337–373. https://doi.org/10.1590/0101-41614925amp
Scherer, K. (2000). Psychological models of emotion. [GS Search]. The Neuropsychology of Emotion, 137, 137–162.
Schmeck, R. R., & Lockhart, D. (1983). Introverts and extraverts require different learning environments. [GS Search]. Educational leadership, 40(5), 54–55.
Shanahan, M. J., Bauldry, S., Roberts, B. W., Macmillan, R., & Russo, R. (2014). Personality and the reproduction of social class. Social Forces, 93(1), 209–240. https://doi.org/10.1093/sf/sou050
Shanahan, M. J., Hill, P. L., Roberts, B. W., Eccles, J., & Friedman, H. S. (2014). Conscientiousness, health, and aging: the life course of personality model. Developmental Psychology, 50(5), 1407. https://doi.org/10.1037/a0031130
Shi, J., Yao, Y., Zhan, C., Mao, Z., Yin, F., & Zhao, X. (2018). The relationship between big five personality traits and psychotic experience in a large non-clinical youth sample: the mediating role of emotion regulation. Frontiers in psychiatry. https://doi.org/10.3389/fpsyt.2018.00648
Snow, R., Corno, L., & Jackson, D. (1996). Individual differences in affective and conative functions. [GS Search]. Handbook of Educational Psychology, 243–310.
Stein, N. L., & Levine, L. J. (1990). Making sense out of emotion: The representation and use of goal-structured knowledge. [GS Search]. Psychological and biological approaches to emotion, 45–73.
Tan, J., Mao, J., Jiang, Y., & Gao, M. (2021). The Influence of Academic Emotions on Learning Effects: A Systematic Review. International Journal of Environmental Research and Public Health, 18(18), 9678. https://doi.org/10.3390/ijerph18189678
Therneau, T., & Lumley, T. (2013). R survival package. [GS Search]. R Core Team.
Thoresen, C. J., Bradley, J. C., Bliese, P. D., & Thoresen, J. D. (2004). The big five personality traits and individual job performance growth trajectories in maintenance and transitional job stages. Journal of applied psychology, 89(5), 835. https://doi.org/10.1037/0021-9010.89.5.835
Turner, J. E., & Schallert, D. L. (2001). Expectancy–value relationships of shame reactions and shame resiliency. Journal of Educational Psychology, 93(2), 320–329. https://doi.org/10.1037/0022-0663.93.2.320
Um, E., Plass, J. L., Hayward, E. O., Homer, B. D., et al. (2012). Emotional design in multimedia learning. Journal of educational psychology, 104(2), 485. https://doi.org/10.1037/a0026609
VanLehn, K. (2006). The behavior of tutoring systems. [GS Search]. International journal of artificial intelligence in education, 16(3), 227–265.
Vedel, A., & Poropat, A. E. (2017). Personality and academic performance. Encyclopedia of personality and individual differences, 1–9. https://doi.org/10.1007/978-3-319-24612-3_989
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Derechos de autor 2023 Diógines D’Avila Goldoni, Helena M. Reis, Patricia Augustin Jaques
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