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Metrics Into Online Course's Critical Success Factors
E-learning has gained tremendous popularity. In recent decades, major universities all over the world offer online courses aiming to support student learning performance, yet often exhibit low completion rates. Faced with the challenge of decreasing learners’ dropout rates, e-learning communities are increasingly in need to improve trainings. Machine learning and data analysis have emerged as powerful methods to analyze educational data in order to enhance Technology Enhanced Learning Environments (TELEs). Most researches are interested in understanding dropout factors related to learners, but few works are committed to refine pedagogical content quality. To address this problem, this paper proposes descriptive statistics analysis to evaluate e-course factors contributing to its success. We take up, at first, the task of analyzing a sample of best online courses. Then, we manage to identify features of successful online courses that are able to attract a large number of learners, meet their needs and improve their satisfaction. We report findings of an exploratory study that investigates the relationship between course success and the strategy of pedagogical content creation.