five

DataSheet1_Training Graduate Students in Multiple Genres of Public and Academic Science Writing: An Assessment Using an Adaptable, Interdisciplinary Rubric.pdf

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://figshare.com/articles/dataset/DataSheet1_Training_Graduate_Students_in_Multiple_Genres_of_Public_and_Academic_Science_Writing_An_Assessment_Using_an_Adaptable_Interdisciplinary_Rubric_pdf/17128064
下载链接
链接失效反馈
官方服务:
资源简介:
There is an urgent need for scientists to improve their communication skills with the public, especially for those involved in applying science to solve conservation or human health problems. However, little research has assessed the effectiveness of science communication training for applied scientists. We responded to this gap by developing a new, interdisciplinary training model, “SciWrite,” based on three central tenets from scholarship in writing and rhetoric: 1) habitual writing, 2) multiple genres for multiple audiences, and 3) frequent review and created an interdisciplinary rubric based on these tenets to evaluate a variety of writing products across genres. We used this rubric to assess three different genres written by 12 SciWrite-trained graduate science students and 74 non-SciWrite-trained graduate science students at the same institution. We found that written work from SciWrite students scored higher than those from non-SciWrite students in all three genres, and most notably thesis/dissertation proposals were higher quality. The rubric results also suggest that the variation in writing quality was best explained by the ability of graduate students to grasp higher-order writing skills (e.g., thinking about audience needs and expectations, clearly describing research goals, and making an argument for the significance of their research). Future programs would benefit from adopting similar training activities and goals as well as assessment tools that take a rhetorically informed approach.
创建时间:
2021-12-06
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作