OARS: exploring instructor analytics for online learning
OARS: exploring instructor analytics for online learning
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OARS：探索在线学习的教师分析
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Candace Thille
中科院分区：
文献类型：
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作者：
J. Bassen;Iris K. Howley;Ethan Fast;John C. Mitchell;Candace Thille
学习分析系统有可能为在线教育带来巨大价值。不幸的是，许多教师和平台在今天的课程中没有充分利用学习分析。在本文中，我们报告这些系统的价值，从课程讲师的角度。我们通过OARS来研究这些想法，OARS是一个模块化的实时学习分析系统，我们将其部署在十多个在线课程中，有数万名学习者。我们利用这个系统作为与不同导师进行半结构化面试的起点。我们的研究为学习分析系统提出了新的设计目标，实时分析对许多教师的重要性，以及教师在使用分析系统时数据选择和聚合的灵活性的价值。
Learning analytics systems have the potential to bring enormous value to online education. Unfortunately, many instructors and platforms do not adequately leverage learning analytics in their courses today. In this paper, we report on the value of these systems from the perspective of course instructors. We study these ideas through OARS, a modular and real-time learning analytics system that we deployed across more than ten online courses with tens of thousands of learners. We leverage this system as a starting point for semi-structured interviews with a diverse set of instructors. Our study suggests new design goals for learning analytics systems, the importance of real-time analytics to many instructors, and the value of flexibility in data selection and aggregation for an instructor when working with an analytics system.