Decrypting the Learners’ Retention Factors in Massive Open Online Courses

Abstract

Massive Open Online Courses (MOOCs) have recently become attractive at most universities, and the number of MOOCs has risen significantly, particularly in India. Despite their popularity, previous research has revealed a low course completion rate and a scarcity of research on the factors that influences learners’ retention in MOOCs. Therefore, it is a good idea to investigate previous research to understand the factors behind the learners’ retention so that an ideal learning model can be created. This study used Structural Equation Modelling to find out the unexplored learner retention factors in MOOCs and create a model, which may extend the satisfaction. MOOC data sets were collected from different Indian universities in Uttarakhand state. This study has explored the majority of influencing factors correlated with learners’ satisfaction. The findings show that MOOC usage intention is influenced by a willingness to credit mobility, the allure of the latest trendy course, content localisation and perceived effectiveness.

Keywords

• Classification, Data-Mining, MOOC, Model, Factors, PLS, Retention, Factor