Uaffect: Um Modelo para Predição de Estados Afetivos em Ambientes Educacionais a partir de Históricos de Contextos
Resumo
Estudos apontam o crescimento e a relevância dos sistemas de reconhecimento de estados afetivos nas mais diferentes áreas. Sabe-se ainda, que existe uma forte relação entre emoções e aprendizagem. Assim, qual a importância das informações de contexto na predição de estados afetivos em ambientes educacionais? Dessa forma, este artigo propõe o Uaffect, um modelo computacional que utiliza históricos de contextos formados em ambientes educacionais para classificação e predição de estados afetivos. O modelo foi avaliado a partir de um estudo quase experimental com 25 alunos. Os resultados de precisão encontrados no melhor cenário são de 89% para estados afetivos positivos e de 61% para estados afetivos negativos. Os testes realizados apresentaram evidências que indicam a possibilidade de uso de históricos de contextos na classificação e predição de estados afetivos em ambientes educacionais, podendo ser usados para prover serviços inteligentes que auxiliem na tomada de decisão no planejamento educacional e regulação afetiva do aluno.
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Picard, R. W. (1997). Affective Computing. Cambridge: MIT Press.
Santos, O. C.; Saneiro, M.; Boticario, J. G.; Rodriguez-Sanchez, M. C. (2016). Toward interactive context-aware affective educational recommendations in computer-assisted language learning, New Review of Hypermedia and Multimedia, 22(1-2), p. 27–57.
Santos, O. C.; Uria-Rivas, R.; Rodriguez-Sanchez, M. C.; Boticario, J. G. (2016). An Open Sensing and Acting Platform for Context-Aware Affective Support in Ambient Intelligent Educational Settings, IEEE Sensors Journal, 16(10), p. 3865–3874.
Sarmiento-Calisaya, E., Ccori, P. C., & Parari, A. C. (2022). An emotion-aware persuasive architecture to support challenging classroom situations. In 2022 IEEE International Conference on Consumer Electronics. p. 1-2.
Satyanarayanan, M. (2001). Pervasive Computing: Vision and Challenges. In: IEEE Personal Communications, 4(8) p. 10-17.
Scherer, K. R. (2000). Psychological models of emotion. The neuropsychology of emotion, v. 137, n. 3, p. 137-162.
Shminan, A. S., & Othman, M. K. (2015). Dynamic student assessment to advocate personalized learning plan. In 2015 International Conference on Information Technology Systems and Innovation p. 1-6.
Uria-Rivas, R., Rodriguez-Sánchez, M. C., Santos, O. C., Vaquero, J., & Boticario, J. G. (2019). Impact of physiological signals acquisition in the emotional support provided in learning scenarios. Sensors, 19(20), 4520.
Wang, Z., Lao, L., Zhang, X., Li, Y., Zhang, T., & Cui, Z. (2022). Context-dependent emotion recognition. Journal of Visual Communication and Image Representation, 89, 103679.
Weiser, M. (1991). The Computer for the 21st Century. Scientific American. v. 265, n. 3, p.94-104.
Aranda, J. A. S.; Bavaresco, R.; Carvalho, J. V.; Yamin, A. C.; Tabares, M. T.; Barbosa, J. L. V. (2021). A computational model for adaptive recording of vital signs through context histories. Journal of Ambient Intelligence and Humanized Computing, v. 1, p. 115.
Benţa, K. I.; Cremene, M.; Gibă, N. R.; Eligio, U. X.; Rarău, A. (2010). Secondary Emotions Deduction from Context, Innovations and Advances In Computer Sciences and Engineering. Springer-Verlag Berlin, Heidelberger Platz 3, D-14197 BERLIN, GERMANY, p. 165+.
Castellano, G.; Leite, I.; Pereira, A.; Martinho, C.; Paiva, A.; Mcowan, P. W. (2010). Inter-ACT: An affective and contextually rich multimodal video corpus for studying interaction with robots, in MM’10 Proceedings of the ACM Multimedia 2010 International Conference, p. 1031–1034.
Chen, J., Yang, T., Huang, Z., Wang, K., Liu, M., & Lyu, C. (2023). Incorporating structured emotion commonsense knowledge and interpersonal relation into contextaware emotion recognition. Applied Intelligence, 53(4), 4201-4217.
Cosenza, R.; Guerra, L. (2011). Neurociência e educação. Artmed Editora
Dey, A. K. (2001). Understanding and Using Context, Personal Ubi Comp, 5(1), p. 4–7.
D’Mello, S.; Calvo, R. A. (2013). Beyond the basic emotions. CHI ’13 Extended Abstracts on Human Factors in Computing Systems on CHI EA ’13, p. 2287.
Dorneles, S. O., Francisco, R., Barbosa, D. N. F., & Barbosa, J. L. V. (2023). Context awareness in recognition of affective states: A systematic mapping of the literature.
International Journal of Human–Computer Interaction, 39(8), 1563-1581.
Eich, L. G. et al. (2022). ESMeRA: a computational model to support Experience Sampling Method (ESM) real-time application. In: XVIII Brazilian Symposium on Information Systems. p. 1-8.
Hoang, M. H.; Kim, S. H.; Yang, H. J.; Lee G. S. (2021). Context-Aware Emotion Recognition Based on Visual Relationship Detection. IEEE Access, 9, 90465-90474.
Kuo, Y. C.; Tseng, C. C. (2011). Recognizing the emotion of learners by physiological sensors to improve english learning performance, in 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), v. 4, p. 2152–2156.
Li, B.; Sano, A. (2020). Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol, 4(2), p. 49:1–49:26.
Martini, B. G.; Helfer, G. A.; Barbosa, J. L. V.; Espinosa Modolo, R. C.; Silva, M. R.; Figueiredo, R. M.; Mendes, A. S.; Leithardt, V. R. Q. (2021). IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories. SENSORS, v. 21, p. 1631.
Moore, P. (2017). Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness? Machines, 5(3), p. 16.
Object-Oriented Architecture SAP: PowerDesigner Documentation Collection. (2018). [link]. Agosto de 2022.
Picard, R. W. (1997). Affective Computing. Cambridge: MIT Press.
Santos, O. C.; Saneiro, M.; Boticario, J. G.; Rodriguez-Sanchez, M. C. (2016). Toward interactive context-aware affective educational recommendations in computer-assisted language learning, New Review of Hypermedia and Multimedia, 22(1-2), p. 27–57.
Santos, O. C.; Uria-Rivas, R.; Rodriguez-Sanchez, M. C.; Boticario, J. G. (2016). An Open Sensing and Acting Platform for Context-Aware Affective Support in Ambient Intelligent Educational Settings, IEEE Sensors Journal, 16(10), p. 3865–3874.
Sarmiento-Calisaya, E., Ccori, P. C., & Parari, A. C. (2022). An emotion-aware persuasive architecture to support challenging classroom situations. In 2022 IEEE International Conference on Consumer Electronics. p. 1-2.
Satyanarayanan, M. (2001). Pervasive Computing: Vision and Challenges. In: IEEE Personal Communications, 4(8) p. 10-17.
Scherer, K. R. (2000). Psychological models of emotion. The neuropsychology of emotion, v. 137, n. 3, p. 137-162.
Shminan, A. S., & Othman, M. K. (2015). Dynamic student assessment to advocate personalized learning plan. In 2015 International Conference on Information Technology Systems and Innovation p. 1-6.
Uria-Rivas, R., Rodriguez-Sánchez, M. C., Santos, O. C., Vaquero, J., & Boticario, J. G. (2019). Impact of physiological signals acquisition in the emotional support provided in learning scenarios. Sensors, 19(20), 4520.
Wang, Z., Lao, L., Zhang, X., Li, Y., Zhang, T., & Cui, Z. (2022). Context-dependent emotion recognition. Journal of Visual Communication and Image Representation, 89, 103679.
Weiser, M. (1991). The Computer for the 21st Century. Scientific American. v. 265, n. 3, p.94-104.
Publicado
06/11/2023
Como Citar
DORNELES, Sandro O.; EICH, Luís Guilherme; BARBOSA, Débora Nice Ferrari; FRANCISCO, Rosemary; BARBOSA, Jorge L. V..
Uaffect: Um Modelo para Predição de Estados Afetivos em Ambientes Educacionais a partir de Históricos de Contextos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 1062-1073.
DOI: https://doi.org/10.5753/sbie.2023.235233.
