O Efeito das Características dos Alunos em Modelos de Dinâmica de Afetos e Detecção Livre de Sensores
Resumo
Este estudo foca-se nas emoções envolvidas na aprendizagem, com o objetivo de investigar a dinâmica afetiva entre estudantes brasileiros e desenvolver modelos de detecção de emoções livres de sensores, considerando características adicionais dos alunos. A pesquisa busca entender como diversos fatores, tais como personalidade, motivação, sexo, comportamentos e a duração das emoções, influenciam na dinâmica afetiva e na detecção das emoções engajamento, confusão, tédio e frustração no âmbito dos Sistemas de Tutoria Inteligentes (STI). A metodologia empregada envolve a coleta de dados por meio do STI PAT2Math, e a utilização do protocolo EmAP-ML para a anotação das emoções. Os resultados principais sugerem que o modelo de dinâmica afetiva aplicado ao contexto brasileiro é análogo aos modelos mais recentes disponíveis na literatura. Ademais, foi possível constatar que as características individuais dos estudantes influenciam nas transições entre suas emoções. Observou-se ainda que a inclusão dessas características como fontes de dados adicionais otimiza o desempenho dos detectores de emoção sem sensores.
Referências
Andres, J. M. A. L., Ocumpaugh, J., Baker, R. S., Slater, S., Paquette, L., Jiang, Y., Karumbaiah, S., Bosch, N., Munshi, A., Moore, A., et al. (2019). Affect sequences and learning in betty’s brain. In Proceedings of the 9th LAK, pages 383–390.
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., and Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. IJAIED, 24(4):387–426.
Azevedo, O., Morais, F., and Jaques, P. A. (2018). Exploring gamification to prevent gaming the system and help refusal in tutoring systems. In European Conference on Technology Enhanced Learning, pages 231–244. Springer.
Baker, R. et al. (2010a). Data mining for education. International encyclopedia of education, 7(3):112–118.
Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., and Graesser, A. C. (2010b). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitiveaffective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4):223–241.
Baker, R. S., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., Aleven, V., Kusbit, G. W., Ocumpaugh, J., and Rossi, L. (2012). Towards sensor-free affect detection in cognitive tutor algebra. EDM.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011a). The dynamics between student affect and behavior occurring outside of educational software. In International Conference on Affective Computing and Intelligent Interaction, pages 14–24. Springer.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011b). The dynamics between student affect and behavior occurring outside of educational software. In International Conference on Affective Computing and Intelligent Interaction, pages 14–24. Springer.
Barbosa, A. A. G. (2009). Modelo hierárquico de fobias infanto-juvenis: testagem e relação com os estilos maternos. PhD thesis, Psicologia Social (UFRN).
Bosch, N. and D’Mello, S. (2017). The affective experience of novice computer programmers. IJAIED, 27(1):181–206.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In AIED, pages 40–51. Springer.
Calvo, R. A. and D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1):18–37.
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.
de Araújo, D. C. S. A., Pereira, S. N., Dos Santos, W. M., dos Santos Menezes, P. W., Rocha, K. S. d. S., Cerqueira-Santos, S., Faro, A., Mesquita, A. R., and de Lyra Jr, D. P. (2021). Brazilian version of the personal report of communication apprehension: Cross-cultural adaptation and psychometric evaluation among healthcare students. PloS one, 16(2):e0246075.
D’Mello, S., Graesser, A., and Taylor, R. S. (2007). Monitoring affective trajectories during complex learning. In Proceedings of the annual meeting of the cognitive science society, volume 29.
Dweck, C. S. and Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological review, 95(2):256.
D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
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., Craig, S. D., Witherspoon, A., Mcdaniel, B., and Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User modeling and user-adapted interaction, 18(1-2):45–80.
Fredrickson, B. L. (1998). What good are positive emotions? Review of General Psychology, 2(3):300–319.
Frenzel, A. C., Pekrun, R., and Goetz, T. (2007). Girls and mathematics—a “hopeless” issue? a control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4):497–514.
Goldoni, D. D., Reis, H. M., and Jaques, P. A. (2022). Modelagem estatística do tempo de permanência de estudantes no estado de confusão através de análise de sobrevivência multivariada. In Anais do XXXIII Simpósio Brasileiro de Informática na Educação (SBIE 2022). Sociedade Brasileira de Computação SBC.
Gouveia, V. V., Diniz, P. K., Santos, W. S. d., Gouveia, R. S., and Cavalcanti, J. P. N. (2008). Metas de realização entre estudantes do ensino médio: evidências de validade fatorial e consistência interna de uma medida. Psicologia: Teoria e Pesquisa, 24(4):535–544.
Graesser, A. and D’Mello, S. K. (2011). Theoretical perspectives on affect and deep learning. In New perspectives on affect and learning technologies, pages 11–21. Springer.
Harley, J. M. (2016). Measuring emotions: a survey of cutting edge methodologies used in computer-based learning environment research. In Emotions, technology, design, and learning, pages 89–114. Elsevier.
Henderson, N., Rowe, J., Paquette, L., Baker, R. S., and Lester, J. (2020). Improving affect detection in game-based learning with multimodal data fusion. In International Conference on Artificial Intelligence in Education, pages 228–239. Springer.
Hutt, S., Grafsgaard, J. F., and D’Mello, S. K. (2019). Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–14.
Jaques, P. A., Seffrin, H., Rubi, G., 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.
Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., Moore, A. L., and Biswas, G. (2018). Expert feature-engineering vs. deep neural networks: which is better for sensor-free affect detection? In AIED, pages 198–211. Springer.
John, O. P. and Srivastava, S. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2(1999):102–138.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., and Andres, A. (2021). A re-analysis and synthesis of data on affect dynamics in learning. IEEE Transactions on Affective Computing.
Linnenbrink-Garcia, L. and Barger, M. M. (2014). Achievement goals and emotions. International handbook of emotions in education, pages 142–161.
Morais, F. and Jaques, P. A. (2021). Dinâmica de afetos dos alunos em um sistema tutor inteligente de matemática no contexto brasileiro. In Anais do XXXII Simpósio Brasileiro de Informática na Educação, pages 691–704. SBC.
Morais, F. and Jaques, P. A. (2022). Dinâmica de afetos em um sistema tutor inteligente de matemática no contexto brasileiro: uma análise da transição de emoções acadêmicas. Revista Brasileira de Informática na Educação, 30:519–541.
Morais, F. and Jaques, P. A. (2023). The dynamics of brazilian students’ emotions in digital learning systems (accepted for publication). International Journal of Artificial Intelligence in Education.
Morais, F., Kautzmann, T. R., Bittencourt, I. I., and Jaques, P. A. (2019). Emap-ml: A protocol of emotions and behaviors annotation for machine learning labels. In EC-TEL, pages 495–509, Netherlands. Springer.
Oakley, A. (2016). Sex, gender and society. Routledge.
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.
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., and Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3):487–501.
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 ITS, pages 1–10. Springer.
Paquette, L., Rowe, J., Baker, R., Mott, B., Lester, J., DeFalco, J., Brawner, K., Sottilare, R., and Georgoulas, V. (2016). Sensor-free or sensor-full: A comparison of data modalities in multi-channel affect detection. In EDM. ERIC.
Pardos, Z. A., Baker, R. S., San Pedro, M. O., Gowda, S. M., and Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. JLA, 1(1):107–128.
Pekrun, R. (2014). Emotions and learning. In Educational practices series. IEA, IBE.
Pekrun, R. (2016). Academic emotions. Handbook of motivation at school, 2:120–144.
Rodrigo, M. M. T., d Baker, R. S., D’Mello, S., Gonzalez, M. C. T., Lagud, M. C., Lim, S. A., Macapanpan, A. F., Pascua, S. A., Santillano, J. Q., Sugay, J. O., et al. (2008). Comparing learners’ affect while using an intelligent tutoring system and a simulation problem solving game. In International Conference on Intelligent Tutoring Systems, pages 40–49. Springer.
Sabourin, J., Rowe, J. P., Mott, B. W., and Lester, J. C. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. In International Conference on Artificial Intelligence in Education, pages 534–536. Springer.
Sinclair, J., Jang, E. E., Azevedo, R., Lau, C., Taub, M., and Mudrick, N. V. (2018). Changes in emotion and their relationship with learning gains in the context of metatutor. In ITS, pages 202–211. Springer.
Tong, E. M. (2010). Personality influences in appraisal–emotion relationships: The role of neuroticism. Journal of Personality, 78(2):393–417.
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.
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., and Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. IJAIED, 24(4):387–426.
Azevedo, O., Morais, F., and Jaques, P. A. (2018). Exploring gamification to prevent gaming the system and help refusal in tutoring systems. In European Conference on Technology Enhanced Learning, pages 231–244. Springer.
Baker, R. et al. (2010a). Data mining for education. International encyclopedia of education, 7(3):112–118.
Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., and Graesser, A. C. (2010b). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitiveaffective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4):223–241.
Baker, R. S., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., Aleven, V., Kusbit, G. W., Ocumpaugh, J., and Rossi, L. (2012). Towards sensor-free affect detection in cognitive tutor algebra. EDM.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011a). The dynamics between student affect and behavior occurring outside of educational software. In International Conference on Affective Computing and Intelligent Interaction, pages 14–24. Springer.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011b). The dynamics between student affect and behavior occurring outside of educational software. In International Conference on Affective Computing and Intelligent Interaction, pages 14–24. Springer.
Barbosa, A. A. G. (2009). Modelo hierárquico de fobias infanto-juvenis: testagem e relação com os estilos maternos. PhD thesis, Psicologia Social (UFRN).
Bosch, N. and D’Mello, S. (2017). The affective experience of novice computer programmers. IJAIED, 27(1):181–206.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. In AIED, pages 40–51. Springer.
Calvo, R. A. and D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1):18–37.
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.
de Araújo, D. C. S. A., Pereira, S. N., Dos Santos, W. M., dos Santos Menezes, P. W., Rocha, K. S. d. S., Cerqueira-Santos, S., Faro, A., Mesquita, A. R., and de Lyra Jr, D. P. (2021). Brazilian version of the personal report of communication apprehension: Cross-cultural adaptation and psychometric evaluation among healthcare students. PloS one, 16(2):e0246075.
D’Mello, S., Graesser, A., and Taylor, R. S. (2007). Monitoring affective trajectories during complex learning. In Proceedings of the annual meeting of the cognitive science society, volume 29.
Dweck, C. S. and Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological review, 95(2):256.
D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
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., Craig, S. D., Witherspoon, A., Mcdaniel, B., and Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User modeling and user-adapted interaction, 18(1-2):45–80.
Fredrickson, B. L. (1998). What good are positive emotions? Review of General Psychology, 2(3):300–319.
Frenzel, A. C., Pekrun, R., and Goetz, T. (2007). Girls and mathematics—a “hopeless” issue? a control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4):497–514.
Goldoni, D. D., Reis, H. M., and Jaques, P. A. (2022). Modelagem estatística do tempo de permanência de estudantes no estado de confusão através de análise de sobrevivência multivariada. In Anais do XXXIII Simpósio Brasileiro de Informática na Educação (SBIE 2022). Sociedade Brasileira de Computação SBC.
Gouveia, V. V., Diniz, P. K., Santos, W. S. d., Gouveia, R. S., and Cavalcanti, J. P. N. (2008). Metas de realização entre estudantes do ensino médio: evidências de validade fatorial e consistência interna de uma medida. Psicologia: Teoria e Pesquisa, 24(4):535–544.
Graesser, A. and D’Mello, S. K. (2011). Theoretical perspectives on affect and deep learning. In New perspectives on affect and learning technologies, pages 11–21. Springer.
Harley, J. M. (2016). Measuring emotions: a survey of cutting edge methodologies used in computer-based learning environment research. In Emotions, technology, design, and learning, pages 89–114. Elsevier.
Henderson, N., Rowe, J., Paquette, L., Baker, R. S., and Lester, J. (2020). Improving affect detection in game-based learning with multimodal data fusion. In International Conference on Artificial Intelligence in Education, pages 228–239. Springer.
Hutt, S., Grafsgaard, J. F., and D’Mello, S. K. (2019). Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–14.
Jaques, P. A., Seffrin, H., Rubi, G., 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.
Jiang, Y., Bosch, N., Baker, R. S., Paquette, L., Ocumpaugh, J., Andres, J. M. A. L., Moore, A. L., and Biswas, G. (2018). Expert feature-engineering vs. deep neural networks: which is better for sensor-free affect detection? In AIED, pages 198–211. Springer.
John, O. P. and Srivastava, S. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2(1999):102–138.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., and Andres, A. (2021). A re-analysis and synthesis of data on affect dynamics in learning. IEEE Transactions on Affective Computing.
Linnenbrink-Garcia, L. and Barger, M. M. (2014). Achievement goals and emotions. International handbook of emotions in education, pages 142–161.
Morais, F. and Jaques, P. A. (2021). Dinâmica de afetos dos alunos em um sistema tutor inteligente de matemática no contexto brasileiro. In Anais do XXXII Simpósio Brasileiro de Informática na Educação, pages 691–704. SBC.
Morais, F. and Jaques, P. A. (2022). Dinâmica de afetos em um sistema tutor inteligente de matemática no contexto brasileiro: uma análise da transição de emoções acadêmicas. Revista Brasileira de Informática na Educação, 30:519–541.
Morais, F. and Jaques, P. A. (2023). The dynamics of brazilian students’ emotions in digital learning systems (accepted for publication). International Journal of Artificial Intelligence in Education.
Morais, F., Kautzmann, T. R., Bittencourt, I. I., and Jaques, P. A. (2019). Emap-ml: A protocol of emotions and behaviors annotation for machine learning labels. In EC-TEL, pages 495–509, Netherlands. Springer.
Oakley, A. (2016). Sex, gender and society. Routledge.
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.
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., and Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3):487–501.
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 ITS, pages 1–10. Springer.
Paquette, L., Rowe, J., Baker, R., Mott, B., Lester, J., DeFalco, J., Brawner, K., Sottilare, R., and Georgoulas, V. (2016). Sensor-free or sensor-full: A comparison of data modalities in multi-channel affect detection. In EDM. ERIC.
Pardos, Z. A., Baker, R. S., San Pedro, M. O., Gowda, S. M., and Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. JLA, 1(1):107–128.
Pekrun, R. (2014). Emotions and learning. In Educational practices series. IEA, IBE.
Pekrun, R. (2016). Academic emotions. Handbook of motivation at school, 2:120–144.
Rodrigo, M. M. T., d Baker, R. S., D’Mello, S., Gonzalez, M. C. T., Lagud, M. C., Lim, S. A., Macapanpan, A. F., Pascua, S. A., Santillano, J. Q., Sugay, J. O., et al. (2008). Comparing learners’ affect while using an intelligent tutoring system and a simulation problem solving game. In International Conference on Intelligent Tutoring Systems, pages 40–49. Springer.
Sabourin, J., Rowe, J. P., Mott, B. W., and Lester, J. C. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. In International Conference on Artificial Intelligence in Education, pages 534–536. Springer.
Sinclair, J., Jang, E. E., Azevedo, R., Lau, C., Taub, M., and Mudrick, N. V. (2018). Changes in emotion and their relationship with learning gains in the context of metatutor. In ITS, pages 202–211. Springer.
Tong, E. M. (2010). Personality influences in appraisal–emotion relationships: The role of neuroticism. Journal of Personality, 78(2):393–417.
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
06/11/2023
Como Citar
MORAIS, Felipe de; JAQUES, Patricia A..
O Efeito das Características dos Alunos em Modelos de Dinâmica de Afetos e Detecção Livre de Sensores. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1166-1179.
DOI: https://doi.org/10.5753/sbie.2023.234402.
