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Automated Writing Evaluation for non-native speaker English academic writing: The case of IADE and its formative feedback

机译:非母语英语学术写作的自动写作评估:IADE案及其形成性反馈

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摘要

This dissertation presents an innovative approach to the development and empirical evaluation of Automated Writing Evaluation (AWE) technology used for teaching and learning. It introduces IADE (Intelligent Academic Discourse Evaluator), a new web-based AWE program that analyzes research article Introduction sections and generates immediate, individualized, discipline-specific feedback. The major purpose of the dissertation was to implement IADE as a formative assessment tool complementing L2 graduate-level academic writing instruction and to investigate the effectiveness and appropriateness of its automated evaluation and feedback. To achieve this goal, the study sought evidence of IADE\u27s Language Learning Potential, Meaning Focus, Learner Fit, and Impact qualities outlined in Chapelle\u27s (2001) CALL evaluation conceptual framework.A mixed-methods approach with a concurrent transformative strategy was employed. Quantitative data consisted of Likert-scale, yes/no, and open-ended survey responses; automated and human scores for first and last drafts; pre-/post test scores; and frequency counts for draft submission and for access to IADE\u27s Help Options. Qualitative data contained students\u27 first and last drafts as well as transcripts of think-aloud protocols and Camtasia computer screen recordings, observations, and semi-structured interviews.The findings indicate that IADE can be considered an effective formative assessment tool suitable for implementation in the targeted instructional context. Its effectiveness was a result of combined strengths of its Language Learning Potential, Meaning Focus, Learner Fit, and Impact qualities, which were all enhanced by the program\u27s automated feedback. The strength of Language Learning Potential was supported by evidence of noticing of and focus on discourse form, improved rhetorical quality of writing, increased learning gains, and relative helpfulness of practice and modified interaction. Learners\u27 focus on the functional meaning of discourse and construction of such meaning served as evidence of strong Meaning Focus. IADE\u27s automated feedback characteristics and Help Options were appropriate for targeted learners, which speaks of adequate Learner Fit. Finally, despite some negative effects caused by IADE\u27s numerical feedback, overall Impact, exerted at affective, intrinsic, pragmatic, and cognitive levels, was found to be positive due to the color-coded type of feedback.The results of this study provide valuable empirical knowledge to the areas of L2 academic writing, AWE, formative assessment, and I/CALL. They have important practical and theoretical implications and are informative for future research as well as for the design and application of new learning technologies.
机译:本文提出了一种创新的方法来开发用于教学的自动写作评估(AWE)技术和经验评估。它介绍了IADE(智能学术话语评估者),这是一个新的基于Web的AWE程序,可对研究文章的“简介”部分进行分析,并生成即时的,个性化的,针对特定学科的反馈。论文的主要目的是将IADE用作形成性评估工具,以补充L2研究生水平的学术写作指导,并研究其自动评估和反馈的有效性和适当性。为了实现这一目标,该研究寻求了Chapelle(u27s)(2001)CALL评估概念框架中概述的IADE语言学习潜能,意义重点,学习者适应度和影响力素质的证据。受雇。定量数据包括李克特量表,是/否和不限成员名额的调查答复;初稿和最后稿的自动评分和人工评分;考试前/考试后分数;以及草稿提交和访问IADE帮助选项的频率计数。定性数据包含学生的初稿和初稿,以及思考协议的抄本和Camtasia计算机屏幕记录,观察结果以及半结构化访谈。研究结果表明,IAED被认为是一种有效的形成性评估工具,适用于有针对性的教学环境。该程序的自动反馈增强了其语言学习潜能,意思重点,学习者适应能力和影响力素质的综合优势,从而提高了其有效性。语言学习潜能的强大得到以下方面证据的支持:注意并关注语篇形式,写作修辞质量提高,学习收益增加以及实践和相对互动的相对帮助。学习者专注于话语的功能意义,这种意义的构建是强烈的意义焦点的证据。 IADE的自动反馈功能和“帮助选项”非常适合有针对性的学习者,说明学习者适合度很高。最后,尽管IADE的数字反馈会产生一些负面影响,但由于颜色编码的反馈类型,因此在情感,内在,语用和认知水平上产生的总体影响是积极的。本研究的结果提供了对L2学术写作,AWE,形成性评估和I / CALL领域有价值的经验知识。它们具有重要的实践和理论意义,并为将来的研究以及新学习技术的设计和应用提供了信息。

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    Cotos, Elena;

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  • 年度 2010
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  • 正文语种 en
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