论文标题
通过可解释的多层表示学习，MOOC在MOOC的数周内辍学预测
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
论文作者
论文摘要
大型开放在线课程（MOOC）已成为在线学习的流行平台。虽然MOOC使学生能够按照自己的节奏学习，但这种灵活性使学生可以轻松退学。在本文中，我们的目标是预测当前一周的ClickStream数据，学习者是否会在下周退出。为此，我们提出了一种基于分支和绑定（BB）算法的多层表示学习解决方案，该解决方案以无监督的方式从低水平的点击式学习中学习，产生可解释的结果，并避免手动功能工程。在Coursera数据的实验中，我们表明我们的模型学习了一个表示形式，该表示允许一个简单的模型与更复杂，特定于任务的模型相似，以及BB算法如何实现可解释的结果。在我们对观察到的局限性的分析中，我们讨论了有希望的未来方向。
Massive Open Online Courses (MOOCs) have become popular platforms for online learning. While MOOCs enable students to study at their own pace, this flexibility makes it easy for students to drop out of class. In this paper, our goal is to predict if a learner is going to drop out within the next week, given clickstream data for the current week. To this end, we present a multi-layer representation learning solution based on branch and bound (BB) algorithm, which learns from low-level clickstreams in an unsupervised manner, produces interpretable results, and avoids manual feature engineering. In experiments on Coursera data, we show that our model learns a representation that allows a simple model to perform similarly well to more complex, task-specific models, and how the BB algorithm enables interpretable results. In our analysis of the observed limitations, we discuss promising future directions.
