Statistical Relational Learning in Student Modeling for Intelligent Tutoring Systems

Abstract

Statistical Relational Learning (SRL) provides a common language to express diverse kinds of learner models for intelligent tutoring systems that are broadly applicable across different domains or applications. It provides new more expressive user modeling capabilities, such as the ability to express (1) probabilistic user models that model causal influence, with feedback loops allowed, (2) logical rules with exceptions, and (3) both hard and soft constraints in first-order logic. Practically, for example, SRL learner models can facilitate building team user models and user models for collaborative instruction by leveraging social network analysis. They can also facilitate building learner models for affective computing that simultaneously model inferences from affect to cognition and cognition to affect.