The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequence trajectories of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.

中文翻译：

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在线课程中任务完成序列的数据驱动建模和表征

学习的内在时间性要求采用能够利用时间序列信息的方法。在这项研究中，我们利用序列数据框架并展示了如何使用数据驱动的在线课程中任务完成时间序列分析来表征个人和团体学习者的行为，并确定给定课程设计中的关键任务和课程会话. 我们还介绍了最近开发的概率贝叶斯模型来学习学生的序列轨迹并预测学生的表现。将我们的数据驱动的基于序列的分析应用于学习在线商业管理课程的学习者的数据，揭示了学习者队列中的不同行为，识别了偏离课程中预期名义顺序的学习者或学习者群体。使用后验的课程成绩，我们探索高绩效和低绩效学习者之间的行为差​​异。我们发现，表现出色的学习者比表现不佳的学习者更经常地遵循每周课程之间的进展，但在每个每周课程中，表现出色的学习者与名义任务顺序的联系较少。然后，我们使用概率贝叶斯模型对高绩效和低绩效学生的序列进行建模，并表明我们可以学习与绩效相关的参与行为。我们还展示了数据序列框架可用于以任务为中心的分析；我们确定课程设计中任务类型之间的关键节点和差异。我们发现非死记硬背的学习任务，例如互动任务或讨论帖，与更高的表现相关。