Clustering students based on the semantic similarity of concept maps
Abstract
Semantically comparing content produced by different students and identifying existing clusters based on this similarity is a major challenge for teachers. This work presents a proposal for the formation of semantic clustering of students from the semantic comparison of conceptual maps constructed by them. Its approach is the automated reading of concept maps, using them as input to vector models of natural language processing that consider thematic and semantic aspects and the context of words. The analyzes carried out make it possible to compare the conceptual models that different individuals have on a subject and plan interactions between them.
Keywords:
Clustering students, Semantic similarity, Concept maps
References
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Ausubel, D. P., Novak, J., & Hanesian, H. (1978). Educational psychology: a cognitive view (2nd ed.). New York: Holt Rinehart and Winston. doi: https://doi.org/10.1037/016814
Barrios, F., López, F., Argerich, L., & Wachenchauzer, R. (2015). Variations of the Similarity Function of TextRank for Automated Summarization. Argentine Symposium on Artificial Intelligence. Buenos Aires. Retrieved from https://arxiv.org/abs/1602.03606
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Retrieved from https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
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. (2019). Fatores que influenciam a aprendizagem assistida em mapas conceituais. XXX Simpósio Brasileiro de Informática na Educação. Brasília.
Boguski, R. R., Cury, D., & Gava, T. (2019). TOM: An intelligent tutor for the construction of knowledge represented in concept maps. Frontiers in Education (FIE). Cincinnati.
Caldas, V. M., & Favero, E. L. (2009). Uma Ferramenta de Avaliação Automática para Mapas Conceituais como Auxílio ao Ensino em Ambientes de Educação a Distância. XX Simpósio Brasileiro de Informática na Educação. doi: http://dx.doi.org/10.5753/cbie.sbie.2009.%25p
Charlet, D., & Damnati, G. (2017). Soft-Cosine Semantic Similarity between Questions for Community Question Answering. Proceedings of the 11th International Workshop on Semantic Evaluation, (pp. 315–319). Vancouver, Canada. doi: http://dx.doi.org/10.18653/v1/S17-2051
Gan, G., Ma, C., & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. SIAM. doi: https://doi.org/10.1137/1.9780898718348
Gómez-Gauchía, H., Díaz-Agudo, B., & Gonzalez-Calero, P. A. (2004). Two-layered approach to knowledge representation using conceptual maps and description logics. Concept Maps: Theory, Methodology, Technology, Proc. of the First Int. Conf. on Concept. Retrieved from http://cmc.ihmc.us/Papers/cmc2004-205.pdf
Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., & Wu, A. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, (pp. 881 - 892). doi: https://doi.org/10.1109/TPAMI.2002.1017616
Kim, S.-W., & Gil, J.-M. (2019). Research paper classification systems based on TF-IDF and LDA schemes. Human-centric Computing and Information Sciences. doi: https://doi.org/10.1186/s13673-019-0192-7
Lamas, F., Boeres, C., Cury, D., & Menezes, C. S. (2005). Comparando mapas conceituais utilizando correspondência de grafos. Simpósio Brasileiro De Informática Na Educação - SBIE, (pp. 24-27).
Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on International Conference on Machine Learning, (pp. 1188-1196). Retrieved from https://arxiv.org/abs/1405.4053
Limongelli, C., Sciarrone, F., Lombardi, M., Marani, A., & Temperini, M. (2017). A framework for comparing concept maps. 16th International Conference on Information Technology Based Higher Education and Training (ITHET). doi: https://doi.org/10.1109/ITHET.2017.8067818
Marcos P. D. Lovati, C. Z. (2017). Clusterizando Mapas Conceituais para Identificar Desempenho Cognitivo em Grupos. VI Congresso Brasileiro de Informática na Educação (CBIE 2017), (pp. 1397-1406). Recife, PE. doi: http://dx.doi.org/10.5753/cbie.sbie.2017.1397
Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing Order into Texts. Conference on Empirical Methods in Natural Language Processing, (pp. 404–411). Barcelona, Spain. Retrieved from https://www.aclweb.org/anthology/W04-3252
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ICLR Workshop Papers. Retrieved from https://arxiv.org/abs/1301.3781
Moreira, R. B., Boguski, R. R., & Cury, D. (2021). Utilizando análise semântica para descobrir implicações significantes em mapas conceituais. ANAIS DO XXXII SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO, (pp. 123-134). doi: https://doi.org/10.5753/sbie.2021.218489
Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic Evaluation of Topic Coherence. The 2010 Annual Conference of the In Human Language Technologies: North American Chapter of the Association for Computational Linguistics (NAACL-HLT ’10), (pp. 100–108). Los Angeles, California. Retrieved from https://www.aclweb.org/anthology/N10-1012
Novak, J., & Gowin, D. (1984). Learning how to learn. Cambridge: Cambridge University Press. doi: https://doi.org/10.1017/CBO9781139173469
Novotný, V. (2018). Implementation Notes for the Soft Cosine Measure. re. In Proceedings of the 27th ACM International Conference on Information and Knowledge Man, (pp. 22-26). Torino, Italy. doi: http://doi.org/10.1145/3269206.3269317
Peters, S., & Shrobe, H. E. (2003). Using Semantic Networks for Knowledge Representation in an Intelligent Environment. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003, (pp. 323-329). doi: https://doi.org/10.1109/PERCOM.2003.1192756
Rios, P. T., Aguiar, C. Z., & Cury, D. (2017). Uma Abordagem construtivista para identificar o conhecimento usando mapa conceitual. VI Congresso Brasileiro de Informática na Educação (CBIE 2017), (pp. 394-403).
Saputra, D. M., Saputra, D., & Oswari, L. D. (2019). Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method. Sriwijaya International Conference on Information Technology and Its Applications. doi: https://doi.org/10.2991/aisr.k.200424.051
Sidorov, G., Gelbukh, A., Gomez-Adorno, H., & Pinto, D. (2014). Soft Similarity and Soft Cosine Measure:Similarity of Features in Vector Space Model. Computación y Sistemas, 491–504. doi: https://doi.org/10.13053/cys-18-3-2043
Simón, A., L. C., & Rosete, A. (2007). Generation of OWL Ontologies from Concept Maps in Shallow Domains. Congresos de la Asociación Española para la Inteligencia Artificial)(CAEPIA), (pp. 259-267). doi: https://doi.org/10.1007/978-3-540-75271-4_27
Singhal, A. (2001). Modern Information Retrieval: A Brief Overview. Bull IEEE Comput Soc Tech Comm Data Eng, (pp. 35-43). Retrieved from http://www1.cs.columbia.edu/~gravano/cs6111/Readings/singhal.pdf
Vygotsky, L. (2007). A formação social da mente. In M. Fontes (Ed.), Interação entre aprendizado e desenvolvimento (7 ed.). São Paulo.
Vygotsky, L. S. (1978). Mind in Society: Development of Higher Psychological Processes. (J.-S. V. Cole M., Ed.) Cambridge, Massachusetts, London, England: Harvard University Press. doi: https://doi.org/10.2307/j.ctvjf9vz4
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. ACM SIGMOD Record. doi: https://doi.org/10.1145/235968.233324
Ausubel, D. P., Novak, J., & Hanesian, H. (1978). Educational psychology: a cognitive view (2nd ed.). New York: Holt Rinehart and Winston. doi: https://doi.org/10.1037/016814
Barrios, F., López, F., Argerich, L., & Wachenchauzer, R. (2015). Variations of the Similarity Function of TextRank for Automated Summarization. Argentine Symposium on Artificial Intelligence. Buenos Aires. Retrieved from https://arxiv.org/abs/1602.03606
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Retrieved from https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
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. (2019). Fatores que influenciam a aprendizagem assistida em mapas conceituais. XXX Simpósio Brasileiro de Informática na Educação. Brasília.
Boguski, R. R., Cury, D., & Gava, T. (2019). TOM: An intelligent tutor for the construction of knowledge represented in concept maps. Frontiers in Education (FIE). Cincinnati.
Caldas, V. M., & Favero, E. L. (2009). Uma Ferramenta de Avaliação Automática para Mapas Conceituais como Auxílio ao Ensino em Ambientes de Educação a Distância. XX Simpósio Brasileiro de Informática na Educação. doi: http://dx.doi.org/10.5753/cbie.sbie.2009.%25p
Charlet, D., & Damnati, G. (2017). Soft-Cosine Semantic Similarity between Questions for Community Question Answering. Proceedings of the 11th International Workshop on Semantic Evaluation, (pp. 315–319). Vancouver, Canada. doi: http://dx.doi.org/10.18653/v1/S17-2051
Gan, G., Ma, C., & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. SIAM. doi: https://doi.org/10.1137/1.9780898718348
Gómez-Gauchía, H., Díaz-Agudo, B., & Gonzalez-Calero, P. A. (2004). Two-layered approach to knowledge representation using conceptual maps and description logics. Concept Maps: Theory, Methodology, Technology, Proc. of the First Int. Conf. on Concept. Retrieved from http://cmc.ihmc.us/Papers/cmc2004-205.pdf
Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., & Wu, A. (2002). An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, (pp. 881 - 892). doi: https://doi.org/10.1109/TPAMI.2002.1017616
Kim, S.-W., & Gil, J.-M. (2019). Research paper classification systems based on TF-IDF and LDA schemes. Human-centric Computing and Information Sciences. doi: https://doi.org/10.1186/s13673-019-0192-7
Lamas, F., Boeres, C., Cury, D., & Menezes, C. S. (2005). Comparando mapas conceituais utilizando correspondência de grafos. Simpósio Brasileiro De Informática Na Educação - SBIE, (pp. 24-27).
Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on International Conference on Machine Learning, (pp. 1188-1196). Retrieved from https://arxiv.org/abs/1405.4053
Limongelli, C., Sciarrone, F., Lombardi, M., Marani, A., & Temperini, M. (2017). A framework for comparing concept maps. 16th International Conference on Information Technology Based Higher Education and Training (ITHET). doi: https://doi.org/10.1109/ITHET.2017.8067818
Marcos P. D. Lovati, C. Z. (2017). Clusterizando Mapas Conceituais para Identificar Desempenho Cognitivo em Grupos. VI Congresso Brasileiro de Informática na Educação (CBIE 2017), (pp. 1397-1406). Recife, PE. doi: http://dx.doi.org/10.5753/cbie.sbie.2017.1397
Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing Order into Texts. Conference on Empirical Methods in Natural Language Processing, (pp. 404–411). Barcelona, Spain. Retrieved from https://www.aclweb.org/anthology/W04-3252
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ICLR Workshop Papers. Retrieved from https://arxiv.org/abs/1301.3781
Moreira, R. B., Boguski, R. R., & Cury, D. (2021). Utilizando análise semântica para descobrir implicações significantes em mapas conceituais. ANAIS DO XXXII SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO, (pp. 123-134). doi: https://doi.org/10.5753/sbie.2021.218489
Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010). Automatic Evaluation of Topic Coherence. The 2010 Annual Conference of the In Human Language Technologies: North American Chapter of the Association for Computational Linguistics (NAACL-HLT ’10), (pp. 100–108). Los Angeles, California. Retrieved from https://www.aclweb.org/anthology/N10-1012
Novak, J., & Gowin, D. (1984). Learning how to learn. Cambridge: Cambridge University Press. doi: https://doi.org/10.1017/CBO9781139173469
Novotný, V. (2018). Implementation Notes for the Soft Cosine Measure. re. In Proceedings of the 27th ACM International Conference on Information and Knowledge Man, (pp. 22-26). Torino, Italy. doi: http://doi.org/10.1145/3269206.3269317
Peters, S., & Shrobe, H. E. (2003). Using Semantic Networks for Knowledge Representation in an Intelligent Environment. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003, (pp. 323-329). doi: https://doi.org/10.1109/PERCOM.2003.1192756
Rios, P. T., Aguiar, C. Z., & Cury, D. (2017). Uma Abordagem construtivista para identificar o conhecimento usando mapa conceitual. VI Congresso Brasileiro de Informática na Educação (CBIE 2017), (pp. 394-403).
Saputra, D. M., Saputra, D., & Oswari, L. D. (2019). Effect of Distance Metrics in Determining K-Value in K-Means Clustering Using Elbow and Silhouette Method. Sriwijaya International Conference on Information Technology and Its Applications. doi: https://doi.org/10.2991/aisr.k.200424.051
Sidorov, G., Gelbukh, A., Gomez-Adorno, H., & Pinto, D. (2014). Soft Similarity and Soft Cosine Measure:Similarity of Features in Vector Space Model. Computación y Sistemas, 491–504. doi: https://doi.org/10.13053/cys-18-3-2043
Simón, A., L. C., & Rosete, A. (2007). Generation of OWL Ontologies from Concept Maps in Shallow Domains. Congresos de la Asociación Española para la Inteligencia Artificial)(CAEPIA), (pp. 259-267). doi: https://doi.org/10.1007/978-3-540-75271-4_27
Singhal, A. (2001). Modern Information Retrieval: A Brief Overview. Bull IEEE Comput Soc Tech Comm Data Eng, (pp. 35-43). Retrieved from http://www1.cs.columbia.edu/~gravano/cs6111/Readings/singhal.pdf
Vygotsky, L. (2007). A formação social da mente. In M. Fontes (Ed.), Interação entre aprendizado e desenvolvimento (7 ed.). São Paulo.
Vygotsky, L. S. (1978). Mind in Society: Development of Higher Psychological Processes. (J.-S. V. Cole M., Ed.) Cambridge, Massachusetts, London, England: Harvard University Press. doi: https://doi.org/10.2307/j.ctvjf9vz4
Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. ACM SIGMOD Record. doi: https://doi.org/10.1145/235968.233324
Published
2022-11-16
How to Cite
BOGUSKI, Rodrigo Ruy; CURY, Davidson.
Clustering students based on the semantic similarity of concept maps. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 33. , 2022, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 979-991.
DOI: https://doi.org/10.5753/sbie.2022.224957.
