Adapting learning activities selection in an intelligent tutoring system to affect

Abstract

My PhD focuses on adapting learning activities selection to learner affect in an intelligent tutoring system. The research aims to investigate the affective states considered for adapting learning activity selection, and how to adapt to these. It also seeks to know how learner’s affective state can be obtained through tutor-learner interaction rather than via sensors or questionnaires. The research will use of a mixture of qualitative and quantitative methods to achieve these aims. This research will significantly contribute to the area of intelligent tutoring technology by providing more insights into how to adapt to affective states, and improve the delivery of learning. The result will lead to an algorithm for learning activity selection based on affect, which also incorporates other relevant learner characteristics, such as personalty, that moderate affect.