Conceptual reinforcement in concept maps using computer inference
Resumo
A importância de um conceito em um mapa conceitual é proporcional à quantidade de relacionamentos em que participa. Adicionando novos relacionamentos a um conceito, estamos destacando-o e reforçando seu papel no mapa, com várias aplicações pedagógicas. Usamos técnicas de processamento de linguagem e regras para inferência automática a partir dos mapas conceituais para identificar e sugerir novos relacionamentos num mapa conceitual. Este artigo apresenta um modelo para encontrar novos relacionamentos em mapas conceituais; mostra uma arquitetura de software que implementa esse modelo e relata alguns casos de teste e perspectivas para trabalhos futuros.
Palavras-chave:
mapa conceitual, inferência computacional, processamento de linguagem, relacionamentos, pedagogia
Referências
Bessmertnyy, I., A., Kulagin, V., S. (2009). Semantic network as a knowledge base in training system. Saint Petersburg State University of Information Technologies, Mechanics and Optics. Saint Petersburg, Russia.
Cañas, A. J., Novak, J. D. (2006). Re-Examining The Foundations for Effective Use of Concept Maps. In A. J. Cañas & J. D. Novak (Eds.), Concept Maps: Theory, Methodology, Technology. Proceedings of the Second International Conference on Concept Mapping (Vol. 1, pp. 494-502). San Jose, Costa Rica: Universidad de Costa Rica.
Kandiko, C., Hay, D., Weller, S. (2013). Concept mapping in the humanities to facilitate reflection: externalizing the relationship between public and personal learning. Arts and Humanities in Higher Education, 12(1), 70-87.
Leake, D. B., Maguitman, A., Reichherzer, T. (2004). Understanding knowledge models: Modeling assessment of concept importance in concept maps. In R. Alterman & D. Kirsch (Eds.), Proceedings of the twenty-sixth annual conference of the cognitive science society (pp. 795-800). Mahwah, NJ: Lawrence Erlbaum.
Lin, T. (2002). Analogical inference over a commonsense database. In Eighteenth national conference on Artificial intelligence, pages 955–956, Menlo Park, CA, USA. American Association for Artificial Intelligence.
Liu, P. L. (2014). Using eye tracking to understand learners' reading process through the concept-mapping learning strategy. Computers & Education, 78, 237-249.
Miller, A. G. (1995). Wordnet: A lexical database for English. Communications of the ACM, 38(11), 39-41.
Novak, J. D., Cañas, A. J. (2006). The Theory Underlying Concept Maps and How to Construct Them (Technical Report No. IHMC CmapTools 2006-01). Pensacola, FL: Institute for Human and Machine Cognition.
Oliver, K. (2009). An investigation of concept mapping to improve the reading comprehension of science texts. Journal of Science Education and Technology, 18(5), 402-414.
Redford, J. S., Thiede, K. W., Wiley, J., Griffin, T. D. (2012). Concept mapping improves metacomprehension accuracy among 7th graders. Learning and Instruction, 22(4), 262-270.
Schuler, K. (2005). "VerbNet: A broad-coverage, comprehensive verb lexicon", Dissertations available from ProQuest. Paper AAI3179808. University of Pennsylvania.
Cañas, A. J., Novak, J. D. (2006). Re-Examining The Foundations for Effective Use of Concept Maps. In A. J. Cañas & J. D. Novak (Eds.), Concept Maps: Theory, Methodology, Technology. Proceedings of the Second International Conference on Concept Mapping (Vol. 1, pp. 494-502). San Jose, Costa Rica: Universidad de Costa Rica.
Kandiko, C., Hay, D., Weller, S. (2013). Concept mapping in the humanities to facilitate reflection: externalizing the relationship between public and personal learning. Arts and Humanities in Higher Education, 12(1), 70-87.
Leake, D. B., Maguitman, A., Reichherzer, T. (2004). Understanding knowledge models: Modeling assessment of concept importance in concept maps. In R. Alterman & D. Kirsch (Eds.), Proceedings of the twenty-sixth annual conference of the cognitive science society (pp. 795-800). Mahwah, NJ: Lawrence Erlbaum.
Lin, T. (2002). Analogical inference over a commonsense database. In Eighteenth national conference on Artificial intelligence, pages 955–956, Menlo Park, CA, USA. American Association for Artificial Intelligence.
Liu, P. L. (2014). Using eye tracking to understand learners' reading process through the concept-mapping learning strategy. Computers & Education, 78, 237-249.
Miller, A. G. (1995). Wordnet: A lexical database for English. Communications of the ACM, 38(11), 39-41.
Novak, J. D., Cañas, A. J. (2006). The Theory Underlying Concept Maps and How to Construct Them (Technical Report No. IHMC CmapTools 2006-01). Pensacola, FL: Institute for Human and Machine Cognition.
Oliver, K. (2009). An investigation of concept mapping to improve the reading comprehension of science texts. Journal of Science Education and Technology, 18(5), 402-414.
Redford, J. S., Thiede, K. W., Wiley, J., Griffin, T. D. (2012). Concept mapping improves metacomprehension accuracy among 7th graders. Learning and Instruction, 22(4), 262-270.
Schuler, K. (2005). "VerbNet: A broad-coverage, comprehensive verb lexicon", Dissertations available from ProQuest. Paper AAI3179808. University of Pennsylvania.
Publicado
30/10/2017
Como Citar
RIBEIRO FILHO, Ernani Leite; TAVARES, Orivaldo de Lira; SILVA DE MENEZES, Crediné; KOWATA, Juliana Hiroko.
Conceptual reinforcement in concept maps using computer inference. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 28. , 2017, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 496-505.
DOI: https://doi.org/10.5753/cbie.sbie.2017.496.
