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Automated Assessment of Quality and Coverage of Ideas in Students' Source-Based Writing

机译：基于学生的基于源的写作的自动评估质量和覆盖

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Source-based writing is an important academic skill in higher education, as it helps instructors evaluate students' understanding of subject matter. To assess the potential for supporting instructors' grading, we design an automated assessment tool for students' source-based summaries with natural language processing techniques. It includes a special-purpose parser that decomposes the sentences into clauses, a pre-trained semantic representation method, a novel algorithm that allocates ideas into weighted content units and another algorithm for scoring students' writing. We present results on three sets of student writing in higher education: two sets of STEM student writing samples and a set of reasoning sections of case briefs from a law school preparatory course. We show that this tool achieves promising results by correlating well with reliable human rubrics, and by helping instructors identify issues in grades they assign. We then discuss limitations and two improvements: a neural model that learns to decompose complex sentences into simple sentences, and a distinct model that learns a latent representation.
机译：基于源的写作是高等教育的重要学术技能，因为它有助于教师评估学生对主题的理解。为了评估支持教师分级的可能性，我们为学生的基于源的摘要设计了自然语言处理技术的自动评估工具。它包括一个特殊的解析器，它将句子分解为条款，一个预先训练的语义表示方法，一种新的算法，它分配给加权内容单元的想法以及用于评分学生写作的另一算法。我们在高等教育中的三套学生写作中提出了结果：两套词干学生写样品和来自法学院筹备课程的一系列案例简报。我们展示该工具通过与可靠的人类规则相关，通过帮助教师识别他们分配等级的问题，实现了有希望的结果。然后，我们讨论局限性和两个改进：一个神经模型，用于将复杂的句子分解为简单的句子，以及学习潜在表示的不同模型。

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