论文标题
使用ClickStream数据分析混合课程中的学生策略
Analyzing Student Strategies In Blended Courses Using Clickstream Data
论文作者
论文摘要
教育软件数据有望对学生的学习行为和成功驱动力的独特见解。尽管大量工作致力于在大规模开放的在线课程中的表现预测，但尚不清楚是否可以将相同的方法应用于混合课程，并且对学生策略的更深入了解通常会缺少。我们使用从自然语言处理（NLP）借用的模式挖掘和模型来了解学生的互动并从混合大学课程中提取频繁的策略。细粒度的点击屏数据是通过Diderot收集的，Diderot是一种跨越广泛功能的非商业教育支持系统。我们发现，根据学生正在准备的评估类型的评估类型，相互作用模式差异很大，并且许多提取的功能可用于可靠的性能预测。我们的结果表明，即使在鉴于足够的数据粒度的混合课程的低数据设置中，提议的混合NLP方法也可以提供有价值的见解。
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Fine-grained clickstream data is collected through Diderot, a non-commercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity.
