Exposía：研究项目提案与同伴反馈的学术写作技能教学与评估
Exposía: Teaching and Assessment of Academic Writing Skills for Research Project Proposals and Peer Feedback
AI总结：
Exposía是首个公开连接写作与反馈的高等教育数据集，用于研究基于教育的计算方法在教学和评估学术写作中的应用。该数据集包含学生研究提案及同伴和教师反馈，通过细粒度评估方案评估写作和反馈。
摘要：
Exposía是首个公开连接写作与反馈的高等教育数据集，用于研究基于教育的计算方法在教学和评估学术写作中的应用。该数据集包含学生研究提案及同伴和教师反馈，通过细粒度评估方案评估写作和反馈。我们使用Exposía对最先进的大语言模型（LLMs）进行基准测试，针对两个任务：自动评分（1）提案和（2）学生评论。我们发现，这两个任务受益于不同的LLMs。此外，闭源模型在课堂环境中更受欢迎，这促使进一步研究改进闭源模型的性能。最后，我们证实了一种提示策略，即同时评分写作的多个方面是最有效的，这对课堂部署具有重要意义。
英文摘要：
We present Exposía, the first public dataset that connects writing and feedback in higher education, enabling research on educationally grounded computational approaches to teaching and evaluating academic writing. Exposía includes student research project proposals and peer and instructor feedback consisting of comments and free-text reviews. The dataset was collected in the "Introduction to Scientific Work" course of the Computer Science. Exposía reflects the multi-stage nature of the academic writing process that includes drafting, receiving feedback, and revising the writing based on the feedback received. Both the project proposals and peer feedback are accompanied by human assessment scores based on a fine-grained, pedagogically-grounded schema for writing and feedback assessment that we develop. We use Exposía to benchmark state-of-the-art large language models (LLMs) on two tasks: automated scoring of (1) the proposals and (2) the student reviews. We find that the two tasks are best served by different LLMs. Furthermore, closed-source models consistently outperform open-weight models, motivating further research on improving the performance of open-weight models preferred in classroom settings. Finally, we establish that a prompting strategy that scores multiple aspects of the writing together is the most effective, paving the way for more effective classroom deployment of modern LLMs.
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