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E-learning papers

espanolLos estudiantes se unen a un curso con la motivacion de persistir durante parte o la totalidad del curso, pero varios factores, como el desgaste o la falta de satisfaccion, pueden llevarlos a desconectarse o abandonar por completo. Las intervenciones educativas dirigidas a estos factores de riesgo puede ayudar a reducir las tasas de abandono. Sin embargo, el diseno de intervencion requiere la capacidad de predecir con precision los abandonos y con tiempo suficiente para permitir la administracion de la intervencion oportuna. En este trabajo, presentamos un predictor de abandono que utiliza caracteristicas de actividad estudiantil para predecir el cual los estudiantes tienen un alto riesgo de abandono. El predictor tiene exito en rojo de pabellon 40% - 50% de los abandonosmientras que estan todavia activos. Un 40% adicional - 45% son marcado dentro de los 14 dias de ausencia del curso. EnglishWhile MOOCs offer educational data on a new scale, many educators have been alarmed by their high dropout rates. Learners 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.

Dropout Prediction in MOOCs using Learner Activity Features