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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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: 22 nov. 2024.

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