Evolução das posturas corporais de estudantes em apresentações de seminários ao longo do semestre: uma análise utilizando dados multimodais e técnicas de clusterização
Resumo
O presente trabalho apresenta uma abordagem baseada em clusterização para identificar os principais padrões corporais em apresentações orais de estudantes em uma disciplina. Utilizando o Microsoft Kinect, foram coletados dados de 43 apresentações de estudantes realizadas em três momentos distintos ao longo do semestre. As 16 características coletadas pelo sistema desenvolvido foram utilizadas como entrada para a clusterização que permitiu identificar três perfis principais de apresentadores: passivos, ativos e semi-ativos. Uma análise sobre a evolução desses perfis aponta que houve uma diminuição do percentual do perfil passivo ao longo do semestre e um aumento do percentual do perfil semi-ativo. Esses tipos de perfis serão integrados ao sistema de coleta de posturas para a futura classificação automática dos apresentadores em tempo real.
Palavras-chave:
posturas corporais, apresentações orais, clusterização, dados multimodais, Kinect
Referências
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Echeverría, V., Avendaño, A., Chiluiza, K., Vásquez, A., and Ochoa, X. (2014). Presentation skills estimation based on video and kinect data analysis. In Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge, MLA 2014, pages 53-60, New York, NY, USA. ACM.
Freund, Y. and Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting.
Junokas, M., Lindgren, R., Kang, J., and Morphew, J. (2018). Enhancing multimodal learning through personalized gesture recognition. Journal of Computer Assisted Learning.
Le, T.-L., Nguyen, M.-Q., et al. (2013). Human posture recognition using human skeleton provided by kinect. In Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, pages 340-345. IEEE.
Leong, C. W., Chen, L., Feng, G., Lee, C. M., and Mulholland, M. (2015). Utilizing depth sensors for analyzing multimodal presentations: Hardware, software and toolkits. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pages 547-556. ACM.
Lloyd, S. (1982). Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129-137.
Muñoz, R., Villarroel, R., Barcelos, T., Souza, A., Merino, E., Guinez, R., and Silva, L. A. (2018). Development of a software that supports multimodal learning analytics: A case study on oral presentations. Journal of Universal Computer Science, 24(2).
Ochoa, X. (2017). Multimodal Learning Analytics. In Lang, C., Siemens, G., Wise, A. F., and Gašević, D., editors, The Handbook of Learning Analytics, pages 129-141. Society for Learning Analytics Research (SoLAR), Alberta, Canada, 1 edition.
Ochoa, X., Domínguez, F., Guamán, B., Maya, R., Falcones, G., and Castells, J. (2018). The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 360-364. ACM.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65.
Schneider, B. and Blikstein, P. (2015). Unraveling students' interaction around a tangible interface using multimodal learning analytics. Journal of Educational Data Mining, 7(3):89-116.
York, D. (2013). Investigating a Relationship between Nonverbal Communication and Student Learning. PhD thesis, Lindenwood University.
Duda, R. O., Hart, P. E., and Stork, D. G. (1995). Pattern classification and scene analysis. 2nd ed. Wiley Interscience.
Echeverría, V., Avendaño, A., Chiluiza, K., Vásquez, A., and Ochoa, X. (2014). Presentation skills estimation based on video and kinect data analysis. In Proceedings of the 2014 ACM Workshop on Multimodal Learning Analytics Workshop and Grand Challenge, MLA 2014, pages 53-60, New York, NY, USA. ACM.
Freund, Y. and Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting.
Junokas, M., Lindgren, R., Kang, J., and Morphew, J. (2018). Enhancing multimodal learning through personalized gesture recognition. Journal of Computer Assisted Learning.
Le, T.-L., Nguyen, M.-Q., et al. (2013). Human posture recognition using human skeleton provided by kinect. In Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, pages 340-345. IEEE.
Leong, C. W., Chen, L., Feng, G., Lee, C. M., and Mulholland, M. (2015). Utilizing depth sensors for analyzing multimodal presentations: Hardware, software and toolkits. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pages 547-556. ACM.
Lloyd, S. (1982). Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129-137.
Muñoz, R., Villarroel, R., Barcelos, T., Souza, A., Merino, E., Guinez, R., and Silva, L. A. (2018). Development of a software that supports multimodal learning analytics: A case study on oral presentations. Journal of Universal Computer Science, 24(2).
Ochoa, X. (2017). Multimodal Learning Analytics. In Lang, C., Siemens, G., Wise, A. F., and Gašević, D., editors, The Handbook of Learning Analytics, pages 129-141. Society for Learning Analytics Research (SoLAR), Alberta, Canada, 1 edition.
Ochoa, X., Domínguez, F., Guamán, B., Maya, R., Falcones, G., and Castells, J. (2018). The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 360-364. ACM.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65.
Schneider, B. and Blikstein, P. (2015). Unraveling students' interaction around a tangible interface using multimodal learning analytics. Journal of Educational Data Mining, 7(3):89-116.
York, D. (2013). Investigating a Relationship between Nonverbal Communication and Student Learning. PhD thesis, Lindenwood University.
Publicado
29/10/2018
Como Citar
VIEIRA, Felipe; CECHINEL, Cristian; MUNOZ, Roberto; LEMOS, Robson; MERINO, Erick; VILLARROEL, Rodolfo.
Evolução das posturas corporais de estudantes em apresentações de seminários ao longo do semestre: uma análise utilizando dados multimodais e técnicas de clusterização. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1483-1492.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1483.
