Método Híbrido para Avaliação de Jogos Baseado na Detecção Automática das Emoções dos Estudantes
Resumo
Uma das abordagens de avaliar diversão em jogos educacionais é analisar sua capacidade de provocar emoções positivas nos estudantes. Esta avaliação geralmente é realizada através de questionários aplicados antes e após a partida. O objetivo deste estudo é propor um método para avaliar jogos educacionais, a partir da associação dos dados de relato pessoal dos estudantes com dados coletados automaticamente dos estados emocionais dos mesmos durante uma partida. Foi realizada uma revisão bibliográfica para embasar a pesquisa e um experimento para validar o método proposto. Os resultados indicam que reconhecer os estados emocionais dos estudantes durante o jogo pode apoiar o desenvolvimento de jogos educacionais motivantes.
Palavras-chave:
avaliação, convolucional, emoção, estudante, jogo educacional, reconhecimento facial
Referências
A. Drachen, P. Mirza-Babaei, L. N. (2018). Games User Research. Oxford Scholarship Online.
Bilal, D. and Bachir, I. (2007). Children’s interaction with cross-cultural and multilingual digital libraries. ii. information seeking, success, and affective experience. Information Processing and Management, 43(1):65 – 80.
Cao, H., Verma, R. and Nenkova, A. (2015). Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech. Computer Speech and Language, 29(1):186–202.
Darrell, T., Gordon, G., Harville, M., and Woodfill, J. (2000). Integrated person tracking using stereo, color, and pattern detection. International Journal of Computer Vision, 37(2):175–185.
Drachen, A., Mirza-Babaei, P., and Nacke, L. E. (2018). Games User Research. Oxford Scholarship Online.
Ekman, P., Friesen, W., O’Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., Krause, R., LeCompte, W., Pitcairn, T., and Bitti, P. R. (1987). Universals and cultural differences in the judgments of facial expressions of emotion. Journal of personality and social psychology, 53:712–7.
Elsattar, H. K. H. A. (2017). Designing for game-based learning model: The effective integration of flow experience and game elements to support learning. In 2017 14th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pages 34–43, Los Alamitos, CA, USA. IEEE Computer Society.
Eseryel, D., Guo, Y., and Law, V. (2012). Interactivity3 Design and Assessment Framework for Educational Games to Promote Motivation and Complex Problem-Solving Skills, pages 257–285.
Hernandez, J., Paredes, P., Roseway, A., and Czerwinski, M. (2014). Under pressure: sensing stress of computer users. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 51–60.
Jabbar, A. I. A. and Felicia, P. (2016). Towards a conceptual framework of GBL Design for engagement and learning of curriculum-based content. International Journal of Game-Based Learning, 6:87–108.
Jovanovic, M., Starcevic, D., Minovic, M., and Stavljanin, V. (2011). Motivation and multimodal interaction in model-driven educational game design. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 41:817 – 824.
Kapoor, A., Burleson, W., and Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65(8):724 – 736.
Kardan, A. and Einavypour, Y. (2008). Involving learner’s emotional behaviors in learning process as a temporary learner model. In Proceeding of International Conference on Virtual Learning (ICVL), Bucurest, Romania.
Kiili, K., de Freitas, S., Arnab, S., and Lainema, T. (2012). The design principles for flow experience in educational games. Procedia Computer Science, 15:78–91.
Lopatovska, I. (2009). Emotional aspects of the online information retrieval process. PhD thesis, Rutgers University-Graduate School-New Brunswick.
Mysirlaki, S. and Paraskeva, F. (2010). Intrinsic motivation and the sense of community in multiplayer games: An extended framework for educational game design. Informatics, Panhellenic Conference on, 0:223–227.
Nickel, K. and Stiefelhagen, R. (2007). Visual recognition of pointing gestures for human robot interaction. Image and Vision Computing, 25(12):1875–1884. The age of human computer interaction.
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., and Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1):36–48. Students’ Emotions and Academic Engagement.
Shimizu, M., Yoshizuka, T., and Miyamoto, H. (2007). A gesture recognition system using stereo vision and arm model fitting. International Congress Series, 1301:89– 92. Brain-Inspired IT III. Invited and selected papers of the 3rd International Conference on Brain-Inspired Information Technology "BrainIT 2006" held in Hibikino, Kitakyushu, Japan between 27 and 29 September 2006.
Truong, K. P., van Leeuwen, D. A., and de Jong, F. M. (2012). Speech-based recognition of self-reported and observed emotion in a dimensional space. Speech Communication, 54(9):1049 – 1063.
Verma, G. K. and Tiwary, U. S. (2014). Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage, 102:162 – 172. Multimodal Data Fusion.
Bilal, D. and Bachir, I. (2007). Children’s interaction with cross-cultural and multilingual digital libraries. ii. information seeking, success, and affective experience. Information Processing and Management, 43(1):65 – 80.
Cao, H., Verma, R. and Nenkova, A. (2015). Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech. Computer Speech and Language, 29(1):186–202.
Darrell, T., Gordon, G., Harville, M., and Woodfill, J. (2000). Integrated person tracking using stereo, color, and pattern detection. International Journal of Computer Vision, 37(2):175–185.
Drachen, A., Mirza-Babaei, P., and Nacke, L. E. (2018). Games User Research. Oxford Scholarship Online.
Ekman, P., Friesen, W., O’Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., Krause, R., LeCompte, W., Pitcairn, T., and Bitti, P. R. (1987). Universals and cultural differences in the judgments of facial expressions of emotion. Journal of personality and social psychology, 53:712–7.
Elsattar, H. K. H. A. (2017). Designing for game-based learning model: The effective integration of flow experience and game elements to support learning. In 2017 14th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pages 34–43, Los Alamitos, CA, USA. IEEE Computer Society.
Eseryel, D., Guo, Y., and Law, V. (2012). Interactivity3 Design and Assessment Framework for Educational Games to Promote Motivation and Complex Problem-Solving Skills, pages 257–285.
Hernandez, J., Paredes, P., Roseway, A., and Czerwinski, M. (2014). Under pressure: sensing stress of computer users. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 51–60.
Jabbar, A. I. A. and Felicia, P. (2016). Towards a conceptual framework of GBL Design for engagement and learning of curriculum-based content. International Journal of Game-Based Learning, 6:87–108.
Jovanovic, M., Starcevic, D., Minovic, M., and Stavljanin, V. (2011). Motivation and multimodal interaction in model-driven educational game design. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 41:817 – 824.
Kapoor, A., Burleson, W., and Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65(8):724 – 736.
Kardan, A. and Einavypour, Y. (2008). Involving learner’s emotional behaviors in learning process as a temporary learner model. In Proceeding of International Conference on Virtual Learning (ICVL), Bucurest, Romania.
Kiili, K., de Freitas, S., Arnab, S., and Lainema, T. (2012). The design principles for flow experience in educational games. Procedia Computer Science, 15:78–91.
Lopatovska, I. (2009). Emotional aspects of the online information retrieval process. PhD thesis, Rutgers University-Graduate School-New Brunswick.
Mysirlaki, S. and Paraskeva, F. (2010). Intrinsic motivation and the sense of community in multiplayer games: An extended framework for educational game design. Informatics, Panhellenic Conference on, 0:223–227.
Nickel, K. and Stiefelhagen, R. (2007). Visual recognition of pointing gestures for human robot interaction. Image and Vision Computing, 25(12):1875–1884. The age of human computer interaction.
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., and Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1):36–48. Students’ Emotions and Academic Engagement.
Shimizu, M., Yoshizuka, T., and Miyamoto, H. (2007). A gesture recognition system using stereo vision and arm model fitting. International Congress Series, 1301:89– 92. Brain-Inspired IT III. Invited and selected papers of the 3rd International Conference on Brain-Inspired Information Technology "BrainIT 2006" held in Hibikino, Kitakyushu, Japan between 27 and 29 September 2006.
Truong, K. P., van Leeuwen, D. A., and de Jong, F. M. (2012). Speech-based recognition of self-reported and observed emotion in a dimensional space. Speech Communication, 54(9):1049 – 1063.
Verma, G. K. and Tiwary, U. S. (2014). Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage, 102:162 – 172. Multimodal Data Fusion.
Publicado
22/11/2021
Como Citar
NASCIMENTO, Nelson N.; CUNHA, Francinete F. da; BRAGA, Juliana C.; GOIS, Joao Paulo.
Método Híbrido para Avaliação de Jogos Baseado na Detecção Automática das Emoções dos Estudantes. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 32. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 350-359.
DOI: https://doi.org/10.5753/sbie.2021.218276.
