数据驱动的在线学习者行为的无监督聚类
Data-driven unsupervised clustering of online learner behaviour
摘要：
在线课程的广泛采用为分析学习者的行为和根据观察到的使用情况优化基于网络的材料提供了机会。在这里，我们介绍了一个数学框架，用于分析从学习者在线参与中收集的时间序列，它允许直接从数据中识别具有相似在线行为的学习者集群，即，该方法使用一个动态时间规整核来创建学习者动作的时间序列之间的成对相似性，并将其与一个无监督的多尺度图聚类算法相结合来聚类具有相似行为模式的学习者组。我们展示了我们的方法在网上参与数据的成人学习者采取六个基于网络的课程作为帝国商学院研究生学位的一部分。我们的分析确定了具有统计学上不同的参与模式的学习者集群，从分布式学习到集中学习，对预先计划的课程结构和/或任务完成的坚持程度不同，并且还揭示了具有高度零星行为的离群学习者。后验比较表明，虽然大多数低绩效学习者的一部分，在大规模的学习集群，高绩效学习者分布在集群与不同的特点的在线参与。我们还表明，我们的方法是能够识别低性能的学习者更准确地比常见的分类方法的基础上从数据中提取的原始统计数据。
英文摘要：
The widespread adoption of online courses opens opportunities for the analysis of learner behaviour and for the optimisation of web-based material adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series collected from online engagement of learners, which allows the identification of clusters of learners with similar online behaviour directly from the data, i.e., the groups of learners are not pre-determined subjectively but emerge algorithmically from the analysis and the data.The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to cluster groups of learners with similar patterns of behaviour. We showcase our approach on online engagement data of adult learners taking six web-based courses as part of a post-graduate degree at Imperial Business School. Our analysis identifies clusters of learners with statistically distinct patterns of engagement, ranging from distributed to massed learning, with different levels of adherence to pre-planned course structure and/or task completion, and also revealing outlier learners with highly sporadic behaviour. A posteriori comparison with performance showed that, although the majority of low-performing learners are part of in the massed learning cluster, the high performing learners are distributed across clusters with different traits of online engagement. We also show that our methodology is able to identify low performing learners more accurately than common classification methods based on raw statistics extracted from the data.
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