Affective e-learning model for recognising learner emotions in online learning environment

Abstract

Today online learning provides wider coverage many different approaches such as distance learning, classroom-based electronic learning and self-access learning. Online learning has been recognized as a support tool for educators and researchers simply it gives is luxury of using at anytime, anywhere. Like any learning process, online learning depends on effective communication of human knowledge, whether this occurs in a face-to-face classroom or across the Internet. Emotions can have enormous affects on learning and play a vital role in decision making, managing learning activities, timing, and reflecting on the studies. Emotions are also important in teaching and learning and often find expression in particular ways, such as interactions with others and motivation in learning. The aim of the research is to develop a computational model for recognizing leaner emotions in online learning environment. The research study was focused on developing a tool to recognise the online learner's emotions. Therefore, the study has developed Online Achievement Emotion Questionnaire (AEQ) based on the AEQ which is suited for the online learning environment. Also the study has identified a methodology for recognising learner performances during learning. That has being measured through six parameters which represent the learner's level of learning during the learning experience. These parameters are analysed using multiple regression analysis and a model equation was developed to compute the online learner's level of learning. 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.