Towards Automatic Tutoring of Custom Student-Stated Math Word Problems

  1. Arnau-González, Pablo
  2. Serrano-Mamolar, Ana
  3. Katsigiannis, Stamos
  4. Arevalillo-Herráez, Miguel
Libro:
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky
  1. Wang, N. (coord.)
  2. Rebolledo-Mendez, G. (coord.)
  3. Dimitrova, V. (coord.)
  4. Matsuda, N. (coord.)
  5. Santos, O.C. (coord.)

Editorial: Springer

ISSN: 1865-0929,  1865-0937

ISBN: 9783031363351,  9783031363368

Año de publicación: 2023

Páginas: 639-644

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-36336-8_99 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Math Word Problem (MWP) solving for teaching math with Intelligent Tutoring Systems (ITSs) faces a major limitation: ITSs only supervise pre-registered problems, requiring substantial manual effort to add new ones. ITSs cannot assist with student-generated problems. To address this, we propose an automated approach to translate MWPs to an ITS’s internal representation using pre-trained language models to convert MWP to Python code, which can then be imported easily. Experimental evaluation using various code models demonstrates our approach’s accuracy and potential for improvement.

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