Improving Student Performance Using Nudge Analytics
Improving Student Performance Using Nudge Analytics
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使用助推分析提高学生表现
DOI：
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发表时间：
2015
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影响因子：
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通讯作者：
Jacqueline L. Feild
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文献类型：
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作者：
Jacqueline L. Feild
为学生的表现提供持续和个性化的反馈是鼓励自我调节学习的重要组成部分。作为我们高等教育平台的一部分，我们构建了一组数据可视化，以向学生提供有关其作业表现的反馈。这些可视化为学生提供了有关他们与班上其他学生相比的表现的信息，并允许他们比较他们在课程中完成作业所花费的时间。反馈中包括“助推”，为学生如何通过调整开始或提交作业的时间来提高成绩提供指导。为了了解向学生提供哪些推动措施，我们分析了超过 140 万学生提交的超过 2700 万份作业的历史数据，以发现学生的表现趋势。数据证实，当作业在截止日期当天开始以及在截止日期之后提交时，学生的成绩会显着下降。我们利用这些发现以及每个学生过去和当前的表现，在可视化中显示与他们相关的提示，突出显示提高未来表现的可行策略。
Providing students with continuous and personalized feedback on their performance is an important part of encouraging self regulated learning. As part of our higher education platform, we built a set of data visualizations to provide feedback to students on their assignment performance. These visualizations give students information about how they are doing compared to the rest of the class, and allow them to compare the time they spent on assignments across their courses. Included in the feedback are ‘nudges’ which provide guidance on how students might improve their performance by adjusting when they start or submit assignments. In order to understand what nudges to provide to students, we analyzed historical data from over 1.4 million students on over 27 million assignment submissions to find student performance trends. The data confirmed that student performance significantly decreases when assignments are started on the same day they are due and when they are submitted after the due date. We used these findings and the past and current performance of each student to display nudges relevant for them in their visualizations, highlighting actionable strategies for improving future performance.