通过推理问题解决策略自动生成提示

C. Piech, M. Sahami, Jonathan Huang, L. Guibas
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引用次数: 139

摘要

探索学生创作作品的整个步骤序列,以及从数千个这样的序列中出现的模式,是对学习更丰富理解的沃土。在本文中,我们使用历史学生数据自动为Code.org“编程一小时”(据我们所知,这是迄今为止最大的在线课程)生成提示。我们首先开发了一系列算法,可以预测专家老师鼓励学生前进的方式。这样的预测可以形成有效提示生成系统的基础。这些算法在再现专家建议方面比目前最先进的方法更准确,易于实现,而且规模也很好。然后,我们展示了激励提示生成算法的相同框架,提出了一个基于序列的统计,可以为每个学习者进行测量。我们发现,这一统计数据对学生未来的成功有很高的预测作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomously Generating Hints by Inferring Problem Solving Policies
Exploring the whole sequence of steps a student takes to produce work, and the patterns that emerge from thousands of such sequences is fertile ground for a richer understanding of learning. In this paper we autonomously generate hints for the Code.org `Hour of Code,' (which is to the best of our knowledge the largest online course to date) using historical student data. We first develop a family of algorithms that can predict the way an expert teacher would encourage a student to make forward progress. Such predictions can form the basis for effective hint generation systems. The algorithms are more accurate than current state-of-the-art methods at recreating expert suggestions, are easy to implement and scale well. We then show that the same framework which motivated the hint generating algorithms suggests a sequence-based statistic that can be measured for each learner. We discover that this statistic is highly predictive of a student's future success.
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