Skip to Main Content
Performing exercises in a simulation-based environment is a convenient and cost-effective way of learning spatial tasks. However, training systems that offer such environments lack models for the assessment of learner's spatial representations and skills, which would allow the automatic generation of customized training scenarios and assistance. Our proposal aims at filling this gap by extending a model for representing learner's cognitive processes in tutoring systems, based on findings from research on spatial cognition. This article describes how the model is applied to represent knowledge handled in complex and demanding tasks, namely, the manipulation of the robotic arm Canadarm2, and, more specifically, how a training system for Canadarm2 manipulation benefits from this model, both by its ability to assess spatial representations and skills and to generate customized assistance and exercises.