Developing knowledge-transforming skills in writing may help students increase learning by actively building knowledge, regardless of the domain. However, many undergraduate students struggle to transform knowledge when drafting essays based on multiple sources. Writing analytics can be used to scaffold knowledge transforming as writers bring evidence to bear in supporting claims. We investigated how to automatically identify sentences representing knowledge transformation in argumentative essays. A synthesis of cognitive theories of writing and Bloom's typology identified 22 linguistic features to model processes of knowledge transforming in a corpus of 38 undergraduates' essays. Findings indicate undergraduates mostly paraphrase or copy information from multiple sources rather than engage deeply with sources' content. Eight linguistic features were important for discriminating evidential sentences as telling versus transforming source knowledge. We trained a machine learning algorithm that accurately classified nearly three of four evidential sentences as knowledge-telling or knowledge-transforming, offering potential for use in future research.

中文翻译：

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多源论证论文中知识转化内容的自动识别

在写作中发展知识转化技能可以帮助学生通过积极构建知识来增加学习，无论领域如何。然而，许多本科生在起草基于多种来源的论文时难以转化知识。写作分析可用于支持知识转化，因为作者提供证据支持主张。我们研究了如何在议论文中自动识别代表知识转化的句子。写作认知理论和布鲁姆类型学的综合确定了 22 种语言特征，以模拟 38 篇本科生论文的语料库中的知识转化过程。调查结果表明，本科生大多从多个来源转述或复制信息，而不是深入了解来源的内容。八个语言特征对于区分证据句子是讲述还是转换源知识很重要。我们训练了一种机器学习算法，该算法将四个证据句子中的近三个准确分类为知识讲述或知识转化，为未来研究提供了可能。