Analytical Analysis of Learners’ Dropout Rate with Data Mining Techniques

Abstract

Massive open online courses (MOOC) are the handy ways for offering the access to quality education especially to those who are pursuing distant education or who wants to enlighten the core of course from the brilliant tutors. The student population has a tendency to be youthful, knowledgeable and MOOC helps students to be a part of different learning methodologies. The basic objective of this work is to understand MOOC environment and analyse the issues related to these courses. The scope of this research work is to understand the major issue of dropout in MOOC courses. Data mining techniques are used to predict this factor which leads to an increase in dropout rates. By identifying and understanding these factors, necessary measures can be taken to push similar types of learners to their maximum potential in the subsequent MOOC courses. This eventually leads to an increase in the completion rate of the MOOC learners.