Auto-affective state detection from text during e-learning session

Abstract

We explore the concept of automatic detection of affective state of a learner in an e learning environment. We propose a model to detect the emotion from learners’ text. We employ machine learning algorithms with ISEAR data and map the affective states against Kort’s spiral learning model. The objective is to construct a model which will determine the learners affective state automatically from text during his/her interaction with e-learning environment. This will help in understanding the learners learning outcomes