Conversational assistant Trigonometric Solving Problems in Natural Language

Authors

  • Neiva Larisane Kuyven Centro Universitário Uniftec
  • Vinícius João de Barros Vanzin Centro Universitário Uniftec
  • Carlos André Antune Centro Universitário Uniftec
  • Alexandra Cemin Centro Universitário Uniftec
  • João Luis Tavares Silva Centro Universitário Uniftec
  • Liane Margarida Rockenbach Tarouco UFRGS

DOI:

https://doi.org/10.5753/rbie.2020.28.0.208

Keywords:

Chatbot, Deep Learning, Intelligent Tutoring Systems, Trigonometry, Teaching and learning

Abstract

Intelligent Tutoring Systems (ITS) are an interdisciplinary area aiming to investigate how to model instructional content based on pedagogical decisions and students’ interactions. Those decisions use student modeling, basedrules domain knowledge and teaching strategies. Most ITS advocate that the feedback of exercises and activities planned by the system define the knowledge students’ level. Therefore, limiting the student’s interactive possibility through the conversational interface. A more "wizard" approach integrates Chatbots into the ITS, using artificial intelligence techniques to manage interactions. This article intends to provide a greater degree of freedom for the student through natural language inputs of problems out of the activity set of the ITS. This work presents a Recurrent Neural Network (RNR) model, which translates trigonometric problems, entered by students, into equation models as part of a larger ITS Trigonometry project. The experiments conducted showed that the proposed model correctly classified a large part of the problems posed by students, providing answers to the proposed problems in a step-bystep resolution format of a Computer Algebra System (CAS).

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References

Chigonga, B. (2016). Learners’ errors when solving trigonometric equations and suggested interventions from grade 12 mathematics teachers. Proceedings of ISTE International Conference on Mathematics, Science and Technology Education. Limpopo, South Africa. pp. 163-176. [GS Search]

Demir, O., Heck, A. (2013). A new learning trajectory for trigonometric functions. In E. Faggiano, & A. Montone (Eds.), Proceedings of the 11th International Conference on Technology in Mathematics Teaching, pp. 119-124. [GS Search]

Fadaee, M, Bisazza, A. & Monz, M. (2017). Data Augmentation for Low-Resource Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vancouver, Canada, pp. 567-573. [DOI:10.18653/v1/P17-2090][GS Search]

Feijó, R.S.A.A. (2018). Dificuldades e obstáculos no aprendizado de Trigonometria: um estudo com alunos do ensino médio do Distrito Federal. Dissertação de Mestrado Profissional em Matemática em Rede Nacional (PROFMAT) da Universidade de Brasília (UNB), Brasília. 108p.

Gliozzo, A.,Ackerson, C., Bhattacharya, R., Goering, A., Jumba, A., Kim, S.Y., Krishnamurthy, L., Lam, T., Littera, A., McIntosh, I., Murthy, S. & Ribas, M.. (2017). Building Cognitive Applications with IBM Watson Services. IBM Redbooks. 132 p. Retrieved From: "http://www.redbooks.ibm.com/redbooks/pdfs/sg248387.pdf"

Graesser, A. C., , VanLehn, K., Rosé, C. P., Jordan, P. W. & Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4), pp. 39-52. [DOI:10.1609/aimag.v22i4.1591][GS Search]

Goodfellow, I., Bengio Y. & Courville, A. Deep Learning. Adaptive Computation and Machine Learning series. MIT Press. 2016. ISBN-13: 978-0262035613. [GS Search]

Greff, K., Srivastava, R.K., Koutník, J. Steunebrink, B. R. & Schmidhuber, J. (2016). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10). [DOI:10.1109/TNNLS.2016.2582924][GS Search]

IBM ILOG. 2013. IBM ILOG CPLEX Optimization Studio 12.6. IBM Europe, Middle East, and Africa Software Announcement ZP13-0553, dated October 1, 2013.

Ishartono, N.; Juniati, D.; Lukito, A. (2016). Developing Mathematics Teaching Devices in the Topic of Trigonometry Based on Guided Discovery Teaching Method, Journal of Research and Advances in Mathematics Education, 2016,1(2), pp. 154-171.

Kamber, D; Takaci, D. (2018). On problematic aspects in learning trigonometry, International Journal of Mathematical Education in Science and Technology, 49(2), pp. 161-175. [DOI:10.1080/0020739X.2017.1357846]

Karpathy, A. (2017). Convolutional neural networks. Retrieved from: http://cs231n.github.io/ convolutional-networks. Access: Dez/2018.

Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O. & Ang, S. D. (2015). Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3, pp. 585-597. ISSN: 2307-387X. [GS Search]

Kushman, N., Artzi, Y., Zettlemoyer, L. & Barzilay, R. (2014). Learning to automatically solve algebra word problems. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Vol. 1, pp. 271-281. ISBN: 978-1-937284-72-5.[GS Search]

LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep Learning. Nature, Vol. 521, pp. 436-444. doi: 10.1038/nature14539. [DOI:10.1038/nature14539] [GS Search]

Martins, F. J., Ferrari, D.N. & Geyer, C.F.R. (2003). jXChat - Um Sistema de Comunicação Eletrônica Inteligente para apoio a Educação a Distância. In SBIE - Brazilian Symposium on Computers in Education, pp.445-454. [DOI:10.5753/cbie.sbie.2003.445-454][GS Search]

Moraes, S. & Machado, R. (2016). Chatterbot for Education: a Study based on Formal Concept Analysis for Instructional Material Recommendation. In SBIE - Brazilian Symposium on Computers in Education, pp.1347-1351. [DOI:10.5753/cbie.sbie.2016.1347][GS Search]

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Roy, S. & Roth, D. (2016). Illinois Math Solver: Math Reasoning on the Web. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 52-56. [DOI: https://doi.org/10.18653/v1/N16-3011][GS Search]

Shi, S., Wang, Y., Lin, C., Liu, X. & Rui, Y. (2015). Automatically solving number word problems by semantic parsing and reasoning. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1132-1142. [DOI:10.18653/v1/D15-1135][GS Search]

SymPy Development Team. (2018). SymPy Documentation. Retrieved from: "http://docs.sympy.org/latest/index.html". Access: Jun/2018.

Tall, D., Vinner, S. (1981). Concept image and concept definition in mathematics with particular reference to limits and continuity. Educational Studies in Mathematics, 12(2), pp.151-169. [GS Search]

Vygotsky, L. S. (2007). A formação social da mente: o desenvolvimento dos processos psicológicos superiores. 7 edição. São Paulo: Martins Fontes.

Wang, Y., Liu, X. & Shi, S. (2017). Deep Neural Solver for Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp.845-854. [DOI:10.18653/v1/D17-1088][GS Search]

Weber, K. (2005). Students’ Understanding of Trigonometric Functions. Mathematics Education Research Journal, 17(3), pp.91-112. [DOI:10.1007/BF03217423][GS Search]

Wenger, E. (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. San Francisco: Morgan Kaufmann.

Wood, D. (2003). The why? what? when? and how? of tutoring: The development of helping and tutoring skills in children. Literacy Teaching and Learning: An International Journal of Early Reading and Writing, 7(1-2), pp.1-30. [GS Search]

Published

2020-02-16

How to Cite

KUYVEN, N. L.; VANZIN, V. J. de B.; ANTUNE, C. A.; CEMIN, A.; SILVA, J. L. T.; TAROUCO, L. M. R. Conversational assistant Trigonometric Solving Problems in Natural Language. Brazilian Journal of Computers in Education, [S. l.], v. 28, p. 208–228, 2020. DOI: 10.5753/rbie.2020.28.0.208. Disponível em: https://journals-sol.sbc.org.br/index.php/rbie/article/view/3862. Acesso em: 7 jul. 2024.

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