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Predicting MOOC Dropout over Weeks Using Machine Learning Methods

机译:使用机器学习方法预测未来几周的MOOC辍学

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With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.
机译:在许多当前的大型在线课程中都发现,辍学率很高,因此能够预测学生辍学的机制变得越来越重要。对于在线论坛上活跃的学生来说,虽然可以部分解决此问题,但对于普通学生来说,情况尚不如此。在本文中,我们提出了一种适用于点击流数据的方法。除其他功能外,机器学习算法还考虑了学生数据的每周历史记录,因此能够注意到学生行为随时间的变化。在课程的后期阶段(即,一旦获得了此类历史数据),这种方法比基线方法能够更好地预测辍学率。

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