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