Intelligent tutoring systems by Bayesian nets with noisy gates

TL;DR

This paper introduces a compact Bayesian net model with noisy logical gates for intelligent tutoring systems, enabling real-time interaction with fewer parameters and faster inference.

Contribution

It proposes a novel parametrization of Bayesian nets using noisy logical gates, reducing complexity and improving inference speed in tutoring systems.

Findings

Reduced number of parameters for Bayesian nets in tutoring systems

Faster inference algorithms for real-time feedback

Effective modeling of uncertainty with logical gates

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.