Retention in Online Courses

Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models

Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on ...

What makes a great MOOC? An interdisciplinary analysis of student retention in online courses

Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained significant popularity among students and educators. Although the broad acceptance of MOOCs, there is still a long way to go in terms of satisfaction of students’ needs, as witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we employ the Grounded Theory Method (GTM) in a quantitative stu...

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 l...

Ringers in Online Mis Courses

Concerns about the potential ‘ringer’ phenomenon (where a student’s online work is done by someone else) are raised frequently about courses deployed wholly or partially online. This study of both fully online and hybrid classes leads to the conclusion that if ringers exists at all, the phenomenon is slight and more prevalent for hybrid classes. While the study is too small for broad generaliza...

Massive Open Online Courses