OER Knowledge CloudJournal ArticleIssue No.37 Experiences and best practices in and around MOOCs20142014/02Halawa, SherifGreene, DanielMitchell, JohnUllmo, Pierre-AntoineKoskinen, Tapioteacher inquiry into student learningorchestrationlearning designlearning analyticsformative assessmentSpaineLearning Paperselearningeuropa.infoEMOOCS 2014 conference37March 20141-101887-1542Learners join a course with the motivation to persist for some or the entire course, but various factors, such as attrition or lack of satisfaction, can lead them to disengage or totally drop out. Educational interventions targeting such risk factors can help reduce dropout rates. However, intervention design requires the ability to predict dropouts accurately and early enough to allow for timely intervention delivery. In this paper, we present a dropout predictor that uses student activity features to predict which students have a high risk of dropout. The predictor succeeds in red-flagging 40% - 50% of dropouts while they are still active. An additional 40% - 45% are red-flagged within 14 days of absence from the course.yeshttp://openeducationeuropa.eu/en/article/Dropout-Prediction-in-MOOCs-using-Learner-Activity-Features?paper=136477http://openeducationeuropa.eu/en/article/Dropout-Prediction-in-MOOCs-using-Learner-Activity-Features?paper=136477In_depth_37_1 (1).pdfBarcelona, Spain