Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks

  title={Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks},
  author={Henrique M. Seffrin and Ig Ibert Bittencourt and Seiji Isotani and Patr{\'i}cia Augustin Jaques},
  journal={2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)},
This paper presents a dynamic Bayesian network model for the assessment of students' algebraic knowledge in step-based intelligent tutoring systems. The proposed work assesses knowledge about concept, skills, and misconceptions of learners. Furthermore, the proposed model is independent of the problems provided by the system (i.e., equations), because it considers the algebraic operation used by the student to solve a step as evidence instead of the final solution provided by the student… Expand
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  • Computer Science
  • Advances in Intelligent Tutoring Systems
  • 2010
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  • J. Reye
  • Computer Science
  • Int. J. Artif. Intell. Educ.
  • 2004
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  • K. VanLehn
  • Computer Science
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  • 2006
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