Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

ABSTRACT

In this paper we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during the interaction with an intelligent learning environment. We describe the probabilistic user model that processes this information, and its formal evaluation. We show that adding eye-tracker information significantly improves the model accuracy on assessing user exploration and self-explanation behaviors.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.1 Computer Uses in Education
          Subjects: Computer-managed instruction (CMI)

General Terms:
Experimentation, Human Factors

Keywords:
adaptive interfaces, eye-tracking, intelligent assistance for complex tasks, intelligent learning environments, meta-cognitive skills, user modeling