In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.
翻译:在大规模开放在线课程(MOOC)中,学生行为的预测模型可以支持学习的多个方面,包括教员反馈和及时干预。当学生结果尚不清楚时,持续课程必须依靠以前提供的课程的历史数据所培训的模式。可以转让模型,但它们的预测性能往往很差。一个原因是这两个课程的预测性特征不能充分代表共同的预测性特征。我们提出了一个自动传输转移学习方法来解决这个问题。它依靠的是MOOC点击流数据的随机化问题认知性组织,每个学生在多个课程中都表示一组具体的MOOC事件类型。它由两种基于与自动编译器的代言学习的替代转移方法组成:一种被动式方法,使用转导式主构件分析,以及一种使用相关校准损失术语的积极方法。我们用这些方法来调查类似和不相近的MOOC的辍学预测的可转移性,并与已知方法进行比较。结果显示模型可改进的可转移性,并表明这些方法能够自动学习体现MOOC的共同预测性特征的特征。