Modelagem Estatística do Tempo de Permanência de Estudantes no Estado de Confusão Através de Análise de Sobrevivência Multivariada
Resumo
Emoções possuem um impacto significativo no processo de aprendizagem. Em especial, a emoção acadêmica da confusão pode possuir um papel tanto positivo quanto negativo durante atividades educacionais. Embora a confusão possa fomentar deliberações intencionais sobre o assunto tratado, um aluno que permanece confuso por um longo período de tempo pode vir a transacionar para emoções acadêmicas puramente negativas. Este artigo apresenta os resultados obtidos através da análise de dados obtidos de 25 alunos que fizeram uso do sistema tutor inteligente PAT2Math e realizaram um teste de personalidade, destacando a influência de diferentes traços de personalidade e desempenho anterior em álgebra no tempo de permanência dos alunos na emoção acadêmica de confusão. Através de modelagem estatística sob a ótica da análise de sobrevivência, foi possível identificar e modelar diferenças estatisticamente significativas no tempo de permanência em confusão de alunos com traços distintos de personalidade e conhecimento prévio de álgebra.
Palavras-chave:
Dinâmica de afeto, Sistemas Tutores Inteligentes, Análise de Sobrevivência, Emoções acadêmicas, Duração da emoção
Referências
Amato, L. (2018). Math anxiety: The impact on traditionally underserved and marginalized adult female undergraduate students in elementary statistics. PhD thesis, Northeastern University of Boston.
Barbosa, A. (2009). Modelo hierárquico de fobias infanto-juvenis: Testagem e relação com os estilos maternos. PhD thesis, Universidade Federal do Rio Grande do Norte.
Bloom, B. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6):4–16.
Brooks, A. W. (2014). Get excited: reappraising pre-performance anxiety as excitement. Journal of Experimental Psychology: General, 143(3):1144.
Ciorbea, I. and Pasarica, F. (2013). The study of the relationship between personality and academic performance. Procedia-Social and Behavioral Sciences, 78:400–404.
Craig, S., Graesser, A., Sullins, J., and 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.
De Feyter, T., Caers, R., Vigna, C., and 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.
Di Leo, I., Muis, K. R., Singh, C. A., and Psaradellis, C. (2019). Curiosity. . . confusion? frustration! the role and sequencing of emotions during mathematics problem solving. Contemporary educational psychology, 58:121–137.
D’mello, S. and Graesser, A. (2013). Autotutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4):1–39.
D’Mello, S., Picard, R.W., and Graesser, A. (2007). Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4):53–61.
D’Mello, S., Taylor, R., and Graesser, A. (2012). Monitoring affective trajectories during complex learning. Encyclopedia of the Sciences of Learning, pages 203–208.
D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
Eagle, M. and Barnes, T. (2014). Survival analysis on duration data in intelligent tutors. In International Conference on Intelligent Tutoring Systems, pages 178–187. Springer.
Goldberg, L. R. (1981). Language and individual differences: The search for universals in personality lexicons. Review of personality and social psychology, 2(1):141–165.
Graesser, A. and D’Mello, S. (2011). Theoretical Perspectives on Affect and Deep Learning. Springer New York, first edition.
Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., and 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.
Jaques, P. A., Nunes, M. A. S., Isotani, S., and Bittencourt, I. (2012). Computação afetiva aplicada a educação: Dotando sistemas tutores inteligentes de habilidades sociais. In Anais do Workshop de Desafios da Computação Aplicada à Educação, pages 50–59.
Jaques, P. A., Seffrin, H., Rubi, G., de Morais, F., Ghilardi, C., Bittencourt, I. I., and 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.
Kassambara, A. (2016). Cox proportional hazards model in r. Available at: <http://www.sthda.com/english/wiki/cox-proportional-hazards-model>. Accessed on: December 28, 2021.
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. Master’s thesis, Universidade do Vale do Rio dos Sinos.
Kobayashi, H. and Hara, F. (1992). Recognition of six basic facial expression and their strength by neural network. In [1992] Proceedings IEEE International Workshop on Robot and Human Communication, pages 381–386. IEEE.
McCrae, R. R. and Costa, P. T. (1983). Social desirability scales: More substance than style. Journal of consulting and clinical psychology, 51(6):882–888.
Miller, A. (1991). Personality types, learning styles and educational goals. Educational psychology, 11(3-4):217–238.
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. Master’s thesis, Universidade do Vale do Rio dos Sinos.
Murphy, L., Eduljee, N. B., Croteau, K., and 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.
Nießen, D., Danner, D., Spengler, M., and 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, pages 1–18.
Nussbaum, E. M. (2002). How introverts versus extroverts approach small-group argumentative discussions. The Elementary School Journal, 102(3):183–197.
Nussbaum, E. M. and 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.
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.
Pekrun, R., Goetz, T., Titz, W., and 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.
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin, 135(2).
Poropat, A. E. (2011). The eysenckian personality factors and their correlations with academic performance. British Journal of Educational Psychology, 81(1):41–58.
Raad, B. and Schouwenburg, H. (1996). Personality in learning and education: a review. European Journal of Personality, 10:303–336.
Rammstedt, B., Danner, D., and Lechner, C. (2017). Personality, competencies, and life outcomes: results from the german piaac longitudinal study. Large-scale assessments in education, 5(1):1–19.
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, pages 180–190. Springer.
Reis, H. M. (2019). Regulação emocional personalizada para sistemas tutores inteligentes por meio de traços de personalidade. PhD thesis, Universidade de São Paulo.
Reis, H. M., Jaques, P. A., and Isotani, S. (2017). Sistemas tutores inteligentes que reconhecem o estado emocional do estudante: Um mapeamento sistemático. Research and Innovation in Brazilian Education, pages 101–114.
Saccaro, A., França, M. T. A., and 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.
Schmeck, R. R. and Lockhart, D. (1983). Introverts and extraverts require different learning environments. Educational leadership, 40(5):54–55.
Seffrin, H., Rubi, G., Ghilardi, C., Morais, F., Jaques, P., Isotani, S., and Bittencourt, I. I. (2012). Dicas inteligentes no sistema tutor inteligente pat2math. In Brazilian symposium on computers in education (Simpósio Brasileiro de Informática na Educação, SBIE), volume 23.
Shanahan, M. J., Bauldry, S., Roberts, B. W., Macmillan, R., and Russo, R. (2014a). Personality and the reproduction of social class. Social Forces, 93(1):209–240.
Shanahan, M. J., Hill, P. L., Roberts, B.W., Eccles, J., and Friedman, H. S. (2014b). Conscientiousness, health, and aging: the life course of personality model. Developmental Psychology, 50(5):1407.
Thoresen, C. J., Bradley, J. C., Bliese, P. D., and 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.
Trivelloni, C. A. and Hochheim, N. (1998). Avaliação de imóveis com técnicas de análise multivariada. In CONGRESSO BRASILEIRO DE CADASTRO TÉCNICO MULTIFINALITÁRIO, volume 3.
VanLehn, K. (2006). The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3):227–265.
Vedel, A. and Poropat, A. E. (2017). Personality and academic performance. Encyclopedia of personality and individual differences, pages 1–9.
Zaslavsky, C. (1994). Fear of math: How to get over it and get on with your life. Rutgers University Press.
Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1):1–27.
Zeidner, M. (1998). Test anxiety: The state of the art. Springer Science & Business Media, first edition.
Barbosa, A. (2009). Modelo hierárquico de fobias infanto-juvenis: Testagem e relação com os estilos maternos. PhD thesis, Universidade Federal do Rio Grande do Norte.
Bloom, B. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6):4–16.
Brooks, A. W. (2014). Get excited: reappraising pre-performance anxiety as excitement. Journal of Experimental Psychology: General, 143(3):1144.
Ciorbea, I. and Pasarica, F. (2013). The study of the relationship between personality and academic performance. Procedia-Social and Behavioral Sciences, 78:400–404.
Craig, S., Graesser, A., Sullins, J., and 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.
De Feyter, T., Caers, R., Vigna, C., and 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.
Di Leo, I., Muis, K. R., Singh, C. A., and Psaradellis, C. (2019). Curiosity. . . confusion? frustration! the role and sequencing of emotions during mathematics problem solving. Contemporary educational psychology, 58:121–137.
D’mello, S. and Graesser, A. (2013). Autotutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4):1–39.
D’Mello, S., Picard, R.W., and Graesser, A. (2007). Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4):53–61.
D’Mello, S., Taylor, R., and Graesser, A. (2012). Monitoring affective trajectories during complex learning. Encyclopedia of the Sciences of Learning, pages 203–208.
D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
Eagle, M. and Barnes, T. (2014). Survival analysis on duration data in intelligent tutors. In International Conference on Intelligent Tutoring Systems, pages 178–187. Springer.
Goldberg, L. R. (1981). Language and individual differences: The search for universals in personality lexicons. Review of personality and social psychology, 2(1):141–165.
Graesser, A. and D’Mello, S. (2011). Theoretical Perspectives on Affect and Deep Learning. Springer New York, first edition.
Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., and 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.
Jaques, P. A., Nunes, M. A. S., Isotani, S., and Bittencourt, I. (2012). Computação afetiva aplicada a educação: Dotando sistemas tutores inteligentes de habilidades sociais. In Anais do Workshop de Desafios da Computação Aplicada à Educação, pages 50–59.
Jaques, P. A., Seffrin, H., Rubi, G., de Morais, F., Ghilardi, C., Bittencourt, I. I., and 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.
Kassambara, A. (2016). Cox proportional hazards model in r. Available at: <http://www.sthda.com/english/wiki/cox-proportional-hazards-model>. Accessed on: December 28, 2021.
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. Master’s thesis, Universidade do Vale do Rio dos Sinos.
Kobayashi, H. and Hara, F. (1992). Recognition of six basic facial expression and their strength by neural network. In [1992] Proceedings IEEE International Workshop on Robot and Human Communication, pages 381–386. IEEE.
McCrae, R. R. and Costa, P. T. (1983). Social desirability scales: More substance than style. Journal of consulting and clinical psychology, 51(6):882–888.
Miller, A. (1991). Personality types, learning styles and educational goals. Educational psychology, 11(3-4):217–238.
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. Master’s thesis, Universidade do Vale do Rio dos Sinos.
Murphy, L., Eduljee, N. B., Croteau, K., and 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.
Nießen, D., Danner, D., Spengler, M., and 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, pages 1–18.
Nussbaum, E. M. (2002). How introverts versus extroverts approach small-group argumentative discussions. The Elementary School Journal, 102(3):183–197.
Nussbaum, E. M. and 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.
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.
Pekrun, R., Goetz, T., Titz, W., and 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.
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin, 135(2).
Poropat, A. E. (2011). The eysenckian personality factors and their correlations with academic performance. British Journal of Educational Psychology, 81(1):41–58.
Raad, B. and Schouwenburg, H. (1996). Personality in learning and education: a review. European Journal of Personality, 10:303–336.
Rammstedt, B., Danner, D., and Lechner, C. (2017). Personality, competencies, and life outcomes: results from the german piaac longitudinal study. Large-scale assessments in education, 5(1):1–19.
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, pages 180–190. Springer.
Reis, H. M. (2019). Regulação emocional personalizada para sistemas tutores inteligentes por meio de traços de personalidade. PhD thesis, Universidade de São Paulo.
Reis, H. M., Jaques, P. A., and Isotani, S. (2017). Sistemas tutores inteligentes que reconhecem o estado emocional do estudante: Um mapeamento sistemático. Research and Innovation in Brazilian Education, pages 101–114.
Saccaro, A., França, M. T. A., and 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.
Schmeck, R. R. and Lockhart, D. (1983). Introverts and extraverts require different learning environments. Educational leadership, 40(5):54–55.
Seffrin, H., Rubi, G., Ghilardi, C., Morais, F., Jaques, P., Isotani, S., and Bittencourt, I. I. (2012). Dicas inteligentes no sistema tutor inteligente pat2math. In Brazilian symposium on computers in education (Simpósio Brasileiro de Informática na Educação, SBIE), volume 23.
Shanahan, M. J., Bauldry, S., Roberts, B. W., Macmillan, R., and Russo, R. (2014a). Personality and the reproduction of social class. Social Forces, 93(1):209–240.
Shanahan, M. J., Hill, P. L., Roberts, B.W., Eccles, J., and Friedman, H. S. (2014b). Conscientiousness, health, and aging: the life course of personality model. Developmental Psychology, 50(5):1407.
Thoresen, C. J., Bradley, J. C., Bliese, P. D., and 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.
Trivelloni, C. A. and Hochheim, N. (1998). Avaliação de imóveis com técnicas de análise multivariada. In CONGRESSO BRASILEIRO DE CADASTRO TÉCNICO MULTIFINALITÁRIO, volume 3.
VanLehn, K. (2006). The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3):227–265.
Vedel, A. and Poropat, A. E. (2017). Personality and academic performance. Encyclopedia of personality and individual differences, pages 1–9.
Zaslavsky, C. (1994). Fear of math: How to get over it and get on with your life. Rutgers University Press.
Zawacki-Richter, O., Marín, V. I., Bond, M., and Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1):1–27.
Zeidner, M. (1998). Test anxiety: The state of the art. Springer Science & Business Media, first edition.
Publicado
16/11/2022
Como Citar
GOLDONI, Diógines D'Avila; REIS, Helena M.; JAQUES, Patrícia A..
Modelagem Estatística do Tempo de Permanência de Estudantes no Estado de Confusão Através de Análise de Sobrevivência Multivariada. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 33. , 2022, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 992-1005.
DOI: https://doi.org/10.5753/sbie.2022.224984.
