使用预测评分模型的形成性论文反馈

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI:10.1145/3097983.3098160

Bronwyn Woods, David Adamson, Shayne Miel, Elijah Mayfield

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引用次数: 50

摘要

中学教育的一个主要组成部分是学习有效地写作，这是一项通过反复练习和形成性指导来加强的技能。然而，在整个学年中，为每个学生提供每篇文章的多个草稿的集中反馈对最敬业的老师来说也是一项挑战。本文首先建立了一个新的有序论文评分模型，并将其与自动论文评分领域的最新结果进行了比较。扩展这个模型，我们描述了一种方法，使用预测现实的文章变体，给作者特定的形成反馈。这种方法在Revision Assistant中使用，Revision Assistant是一个部署的数据驱动的教育产品，它向学生提供即时的、特定于规则的、句子级的反馈，以补充教师的指导。我们提出了这种反馈生成的初步评估，包括离线和部署。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Formative Essay Feedback Using Predictive Scoring Models

A major component of secondary education is learning to write effectively, a skill which is bolstered by repeated practice with formative guidance. However, providing focused feedback to every student on multiple drafts of each essay throughout the school year is a challenge for even the most dedicated of teachers. This paper first establishes a new ordinal essay scoring model and its state of the art performance compared to recent results in the Automated Essay Scoring field. Extending this model, we describe a method for using prediction on realistic essay variants to give rubric-specific formative feedback to writers. This method is used in Revision Assistant, a deployed data-driven educational product that provides immediate, rubric-specific, sentence-level feedback to students to supplement teacher guidance. We present initial evaluations of this feedback generation, both offline and in deployment.

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Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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