Towards the Differentiation of Initial and Final Retention in Massive Open Online Courses

Following an accelerating pace of technological change, Massive Open Online Courses (MOOCs) have emerged as a popular educational delivery platform, leveraging ubiquitous connectivity and computing power to overcome longstanding geographical and financial barriers to education. Consequently, the demographic reach of education delivery is extended towards a global online audience, facilitating learning and development for a continually expanding portion of the world population. However, an extensive literature review indicates that the low completion rate is the major issue related to MOOCs. Due to a lack of in-person interaction between instructors and learners in such courses, the ability of tutors to monitor learners is impaired, often leading to learner withdrawals. To address this problem, learner drop out patterns across five courses offered by Harvard and MIT universities are investigated in this paper. Learning Analytics is applied to address key factors behind participant dropout events through the comparison of attrition during the first and last weeks of each course. The results show that the number of attired participants during the first week of the course is higher than during the last week, low percentages of attired learners are found prior to course closing dates. It is indicated therefore that assessment fees may not represent a significant reason for learners withdrawal. We introduce supervised machine learning algorithms for the analysis of learner retention and attrition within MOOC platform. Results show that machine learning represents a viable direction for the predictive analysis of MOOCs, with highest performances yielded by Boosted Tree classification for initial attrition and Neural Network based classification for final attrition.