论文标题
通过学习点击和视频的学习表示，在MOOC中进行了数周的辍学预测
Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
论文作者
论文摘要
本文解决了MOOC辍学预测中的一个关键挑战，即从ClickStream数据构建有意义的表示形式。据我们所知，已经广泛探索了各种功能提取技术，但在此上下文中，没有任何先前的作品探索了教育内容的建模（例如，视频）及其与学习者的行为（例如点击式流程）的相关性。我们通过设计一种方法来学习视频表示形式以及视频和点击之间的相关性来弥合这一差距。结果表明，建模视频及其与点击的相关性在预测辍学方面具有统计上显着的改进。
This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data. While a variety of feature extraction techniques have been explored extensively for such purposes, to our knowledge, no prior works have explored modeling of educational content (e.g. video) and their correlation with the learner's behavior (e.g. clickstream) in this context. We bridge this gap by devising a method to learn representation for videos and the correlation between videos and clicks. The results indicate that modeling videos and their correlation with clicks bring statistically significant improvements in predicting dropout.
