MAfint: Affective Tutorial Intervention Model for Virtual Learning Environments
Abstract
The teaching and learning process performed virtually has been widespread nowadays with Intelligent Tutoring Systems (ITS), as they can provide immediate and personalized instructions to learners. This personalization can consider the learner affective state to improve teaching strategies. In this work, a tutorial intervention model is presented based on the identification of the type of mathematical error made by the learner. To evaluate the model, an experiment with learners was performed and through the inference of the affective states, the results indicate that personalized interventions provide greater engagement and motivation in comparison to minimal interventions.
Keywords:
Tutorial intervention, Affective States, Intelligent Tutoring Systems
References
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Cooper, D. G., Arroyo, I., e Woolf, B. P. (2011). Actionable affective processing for automatic tutor interventions. In New Perspectives on Affect and Learning Technologies, pages 127–140. Springer New York.
dos Santos, D. C. V.-B. e Falcão, T. P. (2017). Acompanhamento de alunos em ambientes virtuais de aprendizagem baseado em sistemas tutores inteligentes. In (Simpósio Brasileiro de Informática na Educação-SBIE), volume 28, page 1267.
D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chip-Man, P., White, H., Person, N.,Kort, B., Kaliouby, R. E., Picard, R., e Graesser, A. (2008). Autotutor detects and responds to learners affective and cognitive states. Workshop on Emotional and Cognitive Issues at the Int. Conf. Intelligent Tutoring Systems. Montreal, Canada, pages 306–308.
Economides, A. A. (2005). Adaptive feedback evaluation. In Proceedings of the 5th WSEAS International Conference on Distance Learning and Web Engineering.
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Hannafin, M., Land, S., e Oliver, K. (1999). Open learning environments: Foundations, methods, and models. Instructional-design theories and models: A new paradigm of instructional theory, 2:115–140.
Hume, G., Michael, J., Rovick, A., e Evens, M. (1996). Hinting as a tactic in one-on-one tutoring. Journal of the Learning Sciences, 5(1):23–47.
Leite, M. D., Pimentel, A. R., e Pietruchinski, M. H. (2012). Remediação de erros baseada em múltiplas representações externas e classificação de erros aplicada a objetos de aprendizagem inteligentes. In Simpósio Brasileiro de Informática na Educação-SBIE, volume 23.
Marczal, D., Direne, A., Pimentel, A., e Krynski, E. M. (2015). FARMA: Uma ferramenta de autoria para objetos de aprendizagem de conceitos matemáticos. In Anais dos Workshops do IV Congresso Brasileiro de Informática na Educação (CBIE 2015).
McKendree, J. (1990). Effective feedback content for tutoring complex skills. Human–Computer Interaction, 5(4):381–413.
McLoughlin, C. (2004). Achieving excellence in teaching through Scaffolding learner competence. Seeking Educational Excellence, pages 9–10.
Morais, F., Silva, J. D., Reis, H., Isotani, S., e Jaques, P. (2017). Computação afetiva aplicada à educação: uma revisão sistemática das pesquisas publicadas no Brasil. In Anais do XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017).
Movshovitz-Hadar, N., Zaslavsky, O., e Inbar, S. (1987). An empirical classification model for errors in high school mathematics. Journal for research in mathematics Education, pages 3–14.
Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning environments on the basis of the interactive tutoring feedback model. Digital Education Review, 23:7–26.
Peng, A. e Luo, Z. (2009). A framework for examining mathematics teacher knowledge as used in error analysis. For the learning of mathematics, 29(3):22–25.
Picard, R. (1997). Affective computing. Cambridge: MIT Press.
Tiam-Lee, T. J. e Sumi, K. (2018). Adaptive feedback based on student emotion in asystem for programming practice. In Intelligent Tutoring Systems, pages 243–255. Springer International Publishing.
Vanlehn, K. (2006). The behavior of tutoring systems.International journal of artificial intelligence in education, 16(3):227–265.
Cooper, D. G., Arroyo, I., e Woolf, B. P. (2011). Actionable affective processing for automatic tutor interventions. In New Perspectives on Affect and Learning Technologies, pages 127–140. Springer New York.
dos Santos, D. C. V.-B. e Falcão, T. P. (2017). Acompanhamento de alunos em ambientes virtuais de aprendizagem baseado em sistemas tutores inteligentes. In (Simpósio Brasileiro de Informática na Educação-SBIE), volume 28, page 1267.
D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chip-Man, P., White, H., Person, N.,Kort, B., Kaliouby, R. E., Picard, R., e Graesser, A. (2008). Autotutor detects and responds to learners affective and cognitive states. Workshop on Emotional and Cognitive Issues at the Int. Conf. Intelligent Tutoring Systems. Montreal, Canada, pages 306–308.
Economides, A. A. (2005). Adaptive feedback evaluation. In Proceedings of the 5th WSEAS International Conference on Distance Learning and Web Engineering.
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3-4):169–200.
Fleming, M. L. e Levi, W. H. (1993). Instructional message design: Principles from the behavioral and cognitive sciences. Educational technology.
Gottardo, E. e Pimentel, A. (2018). Reconhecimento e adaptação à dinâmica de estados afetivos relacionados à aprendizagem. In Anais do XXIX Simpósio Brasileiro de Informática na Educação (SBIE 2018).
Hannafin, M., Land, S., e Oliver, K. (1999). Open learning environments: Foundations, methods, and models. Instructional-design theories and models: A new paradigm of instructional theory, 2:115–140.
Hume, G., Michael, J., Rovick, A., e Evens, M. (1996). Hinting as a tactic in one-on-one tutoring. Journal of the Learning Sciences, 5(1):23–47.
Leite, M. D., Pimentel, A. R., e Pietruchinski, M. H. (2012). Remediação de erros baseada em múltiplas representações externas e classificação de erros aplicada a objetos de aprendizagem inteligentes. In Simpósio Brasileiro de Informática na Educação-SBIE, volume 23.
Marczal, D., Direne, A., Pimentel, A., e Krynski, E. M. (2015). FARMA: Uma ferramenta de autoria para objetos de aprendizagem de conceitos matemáticos. In Anais dos Workshops do IV Congresso Brasileiro de Informática na Educação (CBIE 2015).
McKendree, J. (1990). Effective feedback content for tutoring complex skills. Human–Computer Interaction, 5(4):381–413.
McLoughlin, C. (2004). Achieving excellence in teaching through Scaffolding learner competence. Seeking Educational Excellence, pages 9–10.
Morais, F., Silva, J. D., Reis, H., Isotani, S., e Jaques, P. (2017). Computação afetiva aplicada à educação: uma revisão sistemática das pesquisas publicadas no Brasil. In Anais do XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017).
Movshovitz-Hadar, N., Zaslavsky, O., e Inbar, S. (1987). An empirical classification model for errors in high school mathematics. Journal for research in mathematics Education, pages 3–14.
Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning environments on the basis of the interactive tutoring feedback model. Digital Education Review, 23:7–26.
Peng, A. e Luo, Z. (2009). A framework for examining mathematics teacher knowledge as used in error analysis. For the learning of mathematics, 29(3):22–25.
Picard, R. (1997). Affective computing. Cambridge: MIT Press.
Tiam-Lee, T. J. e Sumi, K. (2018). Adaptive feedback based on student emotion in asystem for programming practice. In Intelligent Tutoring Systems, pages 243–255. Springer International Publishing.
Vanlehn, K. (2006). The behavior of tutoring systems.International journal of artificial intelligence in education, 16(3):227–265.
Published
2020-11-24
How to Cite
ASCARI, Soelaine Rodrigues; GOTTARDO, Ernani; PIMENTEL, Andrey Ricardo.
MAfint: Affective Tutorial Intervention Model for Virtual Learning Environments. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 832-841.
DOI: https://doi.org/10.5753/cbie.sbie.2020.832.
