在线课程中任务完成序列的数据驱动建模和表征
Data-driven modelling and characterisation of task completion sequences in online courses
摘要：
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，并显示如何在网上课程的任务完成的时间序列的数据驱动的分析，可以用来验证个人和团体学习者的行为，并确定在给定的课程设计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和预测学生的表现。我们的数据驱动的基于序列的分析，从学习者的数据进行在线业务管理课程的应用程序揭示了不同的行为在学习者的队列，识别学习者或学习者群体偏离名义上的顺序预期的过程中。使用课程成绩后验，我们探讨高和低绩效学习者之间的行为差异。我们发现，高性能的学习者遵循每周会议之间的进展比低性能的学习者更有规律，但在每个每周会议高性能的学习者较少绑定到名义任务顺序。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。我们还表明，数据序列框架可以用于以任务为中心的分析，我们确定关键的连接点和课程设计中的任务类型之间的差异。我们发现，非死记硬背的学习任务，如互动任务或讨论帖子，与更高的性能相关。我们讨论了这种分析技术的应用，以帮助课程设计，干预和学生监督。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
