Computational model for affective e-Learning : developing a model for recognising E-Learner's emotions

Abstract

e-Learning is the use of information and communication technology to enable people to learn anytime and anywhere. E-learning is a support tool for educators as well as a medium of delivery of any-time, any-where delivery of content to a dispersed learner community. Emotions are also important in teaching and learning and often find expression in particular ways, such as interactions with others and motivation in learning. Although both emotions and interest can increase learners' likelihood to engage in traditional learning, little is known about the influence of emotions and interest in learning activities in a digital environment. The aim of the research is to develop a computational model for recognizing learner emotions in online learning. Therefore, the study has developed Online Achievement Emotion Questionnaire (AEQ) tool which is suited for the online learning environment. Also the study has identified six parameters which represent the learner's level of learning during the learning experience. Finally the study has analysed and evaluated the correlation between the learner emotions and the observed behaviour. This research study therefore developed a novel model of affective online learning which can be use as a tool to recognise online learner's emotions with regard to the performance in learning.