@inProceedings{
 title = {Dropout Prediction in MOOCs using Learner Activity Features},
 type = {inProceedings},
 year = {2014},
 keywords = {absence,ayse,red-flagging},
 pages = {58-65},
 volume = {37},
 websites = {file:///home/manuel/Downloads/In_depth_37_1.pdf},
 id = {c47de1ae-f846-37ed-a616-c8688d526622},
 created = {2016-09-01T18:51:57.000Z},
 accessed = {2014-04-15},
 file_attached = {false},
 profile_id = {15d5ede6-80ea-3b4c-a7e1-5b79ba86707d},
 group_id = {a7fbd406-18f3-3b0b-bf98-38c617d1e04e},
 last_modified = {2017-03-14T17:40:34.521Z},
 tags = {Intervention,drop-out prediction,drop-out rates,learner motivation,learning analytics},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Halawa2014a},
 private_publication = {false},
 abstract = {While MOOCs offer educational data on a new scale, many educators have been alarmed by their high dropout rates. Learners join a course with some 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 dropout 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 are at risk high 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.},
 bibtype = {inProceedings},
 author = {Halawa, Sherif and Greene, Daniel and Mitchell, John and Mithell, John},
 booktitle = {Proceedings of the European MOOC Stakeholder Summit 2014}
}

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