Framework to improve e-learner satisfaction and further strengthen e-learning implementation

In this study, a framework has been designed to guide institutions to better improve learner satisfaction and further strengthen their e-learning implementation. Undergraduate participants (nź=ź600) completed an online survey of 132 items. This article will first report on the development and validation of an instrument that attempts to reveal factors that affect user satisfaction, and then a multiple regression analysis and a path analysis help further investigate which factors can significantly predict learner satisfaction. The factor analysis identified 14 different factors. These factors were further categorized by the researchers into 6 dimensions i.e. learner dimension, instructor's dimension, course dimension, technology dimension, design dimension, and the environment dimension. The multiple regression analysis showed that e-learners satisfaction can mostly be predicted by learner interaction with others. Findings of this research will help institutions by providing them with psychometric properties that add pedagogical value to e-courses. Important factors that influence e-learner satisfaction in virtual courses.Exploratory factor analysis helped identify important factors and construct a model.Dimensions were learner, instructor, course, technology, design, and environment.Direct method multiple regression analysis to test the effect of different variables.Path analysis to measure model fit and visualize factors in e-learner satisfaction.

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