Using semantic analysis to discover significant implications in concept maps

• Ramon B. Moreira Universidade Federal do Espírito Santo
• Rodrigo R. Boguski Universidade Federal do Espírito Santo
• Davidson Cury Universidade Federal do Espirito Santo

DOI: https://doi.org/10.5753/sbie.2021.218489

Abstract

This paper presents a framework capable of performing semantic analysis of concept maps, through the significant implications defined by Piaget (local, systemic and structural). The objective is to provide a deeper semantic dimension in analyzing the significant implications and extract more accurate information about an individual's knowledge representation and understanding from their map representation. The framework uses natural language processing techniques to predict and word embeddings from the calculation of semantic similarity using neural networks. In order to validate its effectiveness, we carried out experiments in the classroom, obtaining very satisfactory results.

Keywords: concept maps, significant implications, semantic analysis

References

Boguski, R. R., & Cury, D. (2018). Usando regras de associação para a identificação de falhas conceituais. Simpósio Brasileiro de Informática na Educação (SBIE), (pp. 1443-1453). Fortaleza.

Boguski, R. R., Cury, D., and Gava, T. (2019). Tom: An intelligent tutor for the construction of knowledge represented in concept maps. In 2019 IEEE Frontiers in Education Conference (FIE), pages 1–7. IEEE.

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Dutra, Í. M., Johann, S. P., Piccinini, C. A., and da Cruz Fagundes, L. (2006). Uma base de dados para compartilhamento de experiências no uso de mapas conceituais no acompanhamento de processos de conceituação. Novas Tecnologias na Educação, CINTED-UFRGS.

Hao, J.-X., Yan, A., and Chi-Wai, R. (2010). A semantic analysis method for concept map-based knowledge modeling. In Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI2010), Atlantis Press.

Honnibal, M. and Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear, v. 7, n. 1, p. 411-420, 2017.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributedrepresentations of words and phrases and their compositionality. InAdvances in NeuralInformation Processing Systems, pages 3111–3119.

Moreira, M. A. (2012). Mapas conceituais e aprendizagem significativa (concept maps and meaningful learning). Aprendizagem significativa, organizadores prévios, mapas conceituais, digramas V e Unidades de ensino potencialmente significativas, page 41.

Moreira, R., Aguiar, C., and Cury, D. (2019). Ferramenta computacional multilíngue para analisar significações em mapas conceituais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 419.

Novak, J. D. and Cañas, A. J. (2006). The theory underlying concept maps and how to construct and use them. research report 2006-01 Rev 2008-01, Florida Institute for Human and Machine Cognition.

Novak, J. D. and Musonda, D. (1991). A twelve-year longitudinal study of science concept learning. American educational research journal, 28(1):117–153.

Piaget, J. and García, R. (1987). Hacia una lógica de significaciones. Gedisa.

Rios, P., Cury, D., and Dutra, Í. M. (2015). Automatizando uma argumentação construtivista por meio dos mapas conceituais. In XX Congreso Internacional de Informática Educativa (TISE’2015). Disponível em: http://www.tise.cl/volumen11/TISE2015/157-162.pdf.

Rios, P., Teodoro, G., Aguiar, C., and Cury, D. (2017). Uma abordagem construtivista para a identificar o conhecimento usando mapas conceituais. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 28, page 394.

Sorgente, A., Vettigli, G., and Mele, F. (2013). Automatic extraction of cause-effect relations in natural language text. DART@ AI* IA, 2013:37–48.

Souza, N. A. d. and Boruchovitch, E. (2010). Mapas conceituais: estratégia de ensino/aprendizagem e ferramenta avaliativa. Educação em Revista, 26:195–217.