Employing Trait Emotional Intelligence in an Adaptive E-learning Environment

Abstract

This paper investigates possibilities of harnessing trait emotional intelligence in e-learning ecosystems with particular focus on enhancing adaptivity. The goal of the paper is to develop a model for adaptive e-education based on trait emotional intelligence as a criterion. Employing Trait Emotional Intelligence (TraitEI) as a model and agglomerative hierarchical cluster analysis technique, we identify segments of students attending the online course Digital Marketing within the e-learning platform at University of Belgrade, Faculty of Organizational Sciences, Department for E-Business. We found out three important clusters of students exist: those that have "Average TraitEI, Average Performers"; those that have "Slightly above Average TraitEI, High Performers"; and those that have "Above Average TraitEI, Super Performers." The characteristics that most differentiate the super performers group from the rest is the extent to which cluster members have high score of well-being, self-control, emotionality, sociability and a higher record of global TraitEI profiles in general. Comparative analysis using python machine learning packages is used to validate the relevant clusters based on Achievement Emotions Questionnaire (AEQ). The method is found to be useful tool to assist educators in segmenting students and by doing so, online course designers will have the ability to design and develop intervention course materials tailored to better meet the needs of different groups of students. The contribution of this study is reflected in the fact that the proposed model for segmenting students into relevant groups based on emotional intelligence can provide better adaptivity in e-education. In addition, the study can contribute to building more effective educational strategies in an e-learning environment.