Empregando Análise Visual e Sensemaking no Ensino de Predictive Suffix Trees
Resumo
O ensino de conceitos abstratos pode ser bastante complexo. Nesse sentido, compreender Predictive Suffix Trees (PSTs) é particularmente desafiador, uma vez que esse tipo de estrutura de dados, baseada em árvores, armazena e relaciona simultaneamente informações sobre espaço, tempo e probabilidades. Esse trabalho apresenta um experimento prático que visa tornar mais aplicado o ensino de PSTs, baseando-se no desenvolvimento de uma aplicação específica, que é testada e analisada quantitativa e qualitativamente, demonstrando que a solução proposta pode ser uma alternativa educacional viável.
Palavras-chave:
PSTs, árvores de sufixos preditivos, visualização de dados, sensemaking, educação em ciência da computação
Referências
Aigner, W., Miksch, S., Müller, W., Schumann, H., and Tominski, C. (2008). Visual methods for analyzing time-oriented data. IEEE transactions on visualization and computer graphics, 14(1):47–60.
Beheshti, E., Villanosa, K., and Horn, M. (2018). Understanding parent-child sensemaking around interactive museum exhibits. Annual Meeting of the American Educational Research Association (AERA 2018).
Blackwell, A. F. (2006). The reification of metaphor as a design tool. ACM Transactions on Computer-Human Interaction (TOCHI), 13(4):490–530.
Bobis, J. and Way, J. (2018). Building connections between children’s representations and their conceptual development in mathematics. In Forging Connections in Early Mathematics Teaching and Learning, pages 55–72. Springer.
Brown, J. S., Collins, A., and Duguid, P. (1989). Situated cognition and the culture of learning. Educational researcher, 18(1):32–42.
Caglayan, G. (2018). Coordinating analytic and visual approaches: Math majors’ understanding of orthogonal hermite polynomials in the inner product space pnp in a technology-assisted learning environment. The Journal of Mathematical Behavior.
Dervin, B. (1983). An overview of sense-making research: Concepts, methods, and results to date. The Author.
Fabrikant, S. I. and Lobben, A. (2009). Introduction: Cognitive issues in geographic information visualization. Cartographica: The International Journal for Geographic Information and Geovisualization, 44(3):139–143.
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., and Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Information visualization, pages 154–175. Springer.
Leite, A. J. M., Santos, E., Vidal, C. A., and De Macêdo, J. A. F. (2017). Visual analysis of predictive suffix trees for discovering movement patterns and behaviors. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 103–110. IEEE.
Martínez, M. (2011). Travelling in branching time. Disputatio, 4(31):271–287.
Ng, O.-L., Sinclair, N., and Davis, B. (2018). Drawing off the page: How new 3d technologies provide insight into cognitive and pedagogical assumptions about mathematics. The Mathematics Enthusiast, 15(3):563–578.
Rocha, C. L., Brilhante, I. R., Lettich, F., De Macedo, J. A. F., Raffaeta, A., Andrade, R., and Orlando, S. (2016). Tpred: a spatio-temporal location predictor framework. In Proceedings of the 20th International Database Engineering & Applications Symposium, pages 34–42. ACM.
Ron, D., Singer, Y., and Tishby, N. (1994). Learning probabilistic automata with variable memory length. In Proceedings of the seventh annual conference on Computational learning theory, pages 35–46. ACM.
Russell, D. M., Stefik, M. J., Pirolli, P., and Card, S. K. (1993). The cost structure of sensemaking. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems, pages 269–276. ACM.
Siemens, G. and Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5):30.
Warburton, E. C. and Laughlin, G. (2018). A performed solution to the pythagorean problem: The three bodies project. Leonardo, pages 1–12.
Beheshti, E., Villanosa, K., and Horn, M. (2018). Understanding parent-child sensemaking around interactive museum exhibits. Annual Meeting of the American Educational Research Association (AERA 2018).
Blackwell, A. F. (2006). The reification of metaphor as a design tool. ACM Transactions on Computer-Human Interaction (TOCHI), 13(4):490–530.
Bobis, J. and Way, J. (2018). Building connections between children’s representations and their conceptual development in mathematics. In Forging Connections in Early Mathematics Teaching and Learning, pages 55–72. Springer.
Brown, J. S., Collins, A., and Duguid, P. (1989). Situated cognition and the culture of learning. Educational researcher, 18(1):32–42.
Caglayan, G. (2018). Coordinating analytic and visual approaches: Math majors’ understanding of orthogonal hermite polynomials in the inner product space pnp in a technology-assisted learning environment. The Journal of Mathematical Behavior.
Dervin, B. (1983). An overview of sense-making research: Concepts, methods, and results to date. The Author.
Fabrikant, S. I. and Lobben, A. (2009). Introduction: Cognitive issues in geographic information visualization. Cartographica: The International Journal for Geographic Information and Geovisualization, 44(3):139–143.
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., and Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Information visualization, pages 154–175. Springer.
Leite, A. J. M., Santos, E., Vidal, C. A., and De Macêdo, J. A. F. (2017). Visual analysis of predictive suffix trees for discovering movement patterns and behaviors. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pages 103–110. IEEE.
Martínez, M. (2011). Travelling in branching time. Disputatio, 4(31):271–287.
Ng, O.-L., Sinclair, N., and Davis, B. (2018). Drawing off the page: How new 3d technologies provide insight into cognitive and pedagogical assumptions about mathematics. The Mathematics Enthusiast, 15(3):563–578.
Rocha, C. L., Brilhante, I. R., Lettich, F., De Macedo, J. A. F., Raffaeta, A., Andrade, R., and Orlando, S. (2016). Tpred: a spatio-temporal location predictor framework. In Proceedings of the 20th International Database Engineering & Applications Symposium, pages 34–42. ACM.
Ron, D., Singer, Y., and Tishby, N. (1994). Learning probabilistic automata with variable memory length. In Proceedings of the seventh annual conference on Computational learning theory, pages 35–46. ACM.
Russell, D. M., Stefik, M. J., Pirolli, P., and Card, S. K. (1993). The cost structure of sensemaking. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems, pages 269–276. ACM.
Siemens, G. and Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5):30.
Warburton, E. C. and Laughlin, G. (2018). A performed solution to the pythagorean problem: The three bodies project. Leonardo, pages 1–12.
Publicado
29/10/2018
Como Citar
LEITE JÚNIOR, Antonio José M.; SANTOS, Emanuele; VIDAL, Creto A.; ROCHA, Cleilton Lima.
Empregando Análise Visual e Sensemaking no Ensino de Predictive Suffix Trees. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1043-1052.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1043.
