Intelligent tutoring systems by Bayesian nets with noisy gates
Additional information
Authors
Antonucci A.,
Mangili F.,
Bonesana C.,
Adorni G.
Type
Article in conference proceedings
Year
2021
Language
English
Abstract
Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of parameters might be important for multiple reasons. First, as these models are typically based on expert knowledge, a huge number of parameters to elicit might discourage practitioners from adopting them. Moreover, the number of model parameters affects the complexity of the inferences, while a fast computation of the queries is needed for real-time feedback. We advocate logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems. We discuss the semantics of the model parameters to elicit and the assumptions required to apply such approach in this domain. We also derive a dedicated inference scheme to speed up computations.
Keywords
Bayesian networks, Intelligent tutoring systems, Noisy-OR gates
Conference proceedings
Proceedings of FLAIRS-35
Numero ( Mese )
2022
Volume
35
Diffusion
License
CC BY-NC
Visibility
Public
Status open access
Gold
