学习用来决定如何教学的特征

Min Hyung Lee, Joe Runde, Warfa Jibril, Zhuoying Wang, E. Brunskill
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引用次数: 5

摘要

作为扩大个性化教学的一步,我们试图自动识别教师在教授单个学生时用于选择下一个活动的交互式学习过程的关键特征。这些特征既可以为设计用于促进教学决策的计算学生模型提供信息,也可以帮助实现利用这些已识别的特征集的自动自我改进的教学系统。我们提出的初步结果表明,在教授学生直方图时,一个非常小的特征集几乎与一个大得多的特征集一样好,可以预测人类导师的决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning the Features Used To Decide How to Teach
As a step towards scaling personalized instruction, we seek to automatically identify the key features of the interactive learning process teachers use to select the next activity when teaching a single student. Such features could both inform computational student models designed to facilitate instructional decisions, and help enable automated self-improving teaching systems that leverage this identified feature set. We present preliminary results that a very small set of features is almost as good as a much larger set of features at predicting human tutor decisions when teaching students about histograms.
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