MOOCs — massive open online courses — grant huge numbers of people access to world-class educational resources, but they also suffer high rates of attrition.
To some degree, that’s inevitable: Many people who enroll in MOOCs may have no interest in doing homework, but simply plan to listen to video lectures in their spare time.
Others, however, may begin courses with the firm intention of completing them but get derailed by life’s other demands. Identifying those people before they drop out and providing them with extra help could make their MOOC participation much more productive.
The problem is that you don’t know who’s actually dropped out until the MOOC has been completed. One missed deadline does not a drop-out make; but after the second or third missed deadline, it may be too late for an intervention to do any good.
MIT researchers recently developed a dropout-prediction model trained on data from one offering of a course can help predict which students will stop out of the next offering. The prediction remains fairly accurate even if the organization of the course changes, so that the data collected during one offering doesn’t exactly match the data collected during the next.
“There’s a known area in machine learning called transfer learning, where you train a machine-learning model in one environment and see what you have to do to adapt it to a new environment,” says CSAIL research scientist Kalyan Veeramachaneni. “Because if you’re not able to do that, then the model isn’t worth anything, other than the insight it may give you. It cannot be used for real-time prediction.”
Read more at MIT News: http://newsoffice.mit.edu/2015/at-risk-students-moocs-dropout-0701