Towards a Model that Considers the Student's Affective Dimensions in Intelligent Learning Environments

Abstract

This chapter introduces the affectivity as decision support to the action of tutor agent. It argues that computational teaching system should take into account affective factors to do the interaction more flexibly; and that a computational architecture interacting with humans must explicitly preview beliefs and affective reasoning. It is defined as an architecture to support an agent in a way of recognizing some affective factors that represent action of humans in interaction with artificial agents. The agent is modelled through mental states and is responsible for high-level reasoning. It is presented that the cognitive evaluation of emotional situations allows more flexible actions of a system due to its adaptability to human agents. Furthermore, the author hopes that these studies will also bring contributions about which and how emotions are really involved in teaching and learning situations where one of the partners is an artificial agent.