当前位置: X-MOL 学术Assess. Eval. High. Educ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Learning to improve the quality peer feedback through experience with peer feedback
Assessment & Evaluation in Higher Education ( IF 4.1 ) Pub Date : 2020-10-18 , DOI: 10.1080/02602938.2020.1833179
Zheng Zong 1 , Christian D. Schunn 2 , Yanqing Wang 1
Affiliation  

Abstract

Peer review is regularly found to be a powerful and efficient technique for assessment and feedback, but many students are inexperienced and sometimes struggle to provide meaningful feedback. It is considered best practice to provide students with some training on how to be a good reviewer, but few classes can afford to devote much time to such training, and the assumption is that review quality will improve with experience. This study directly examines what kinds of experiences during peer feedback activities improve reviewing quality. In particular, organized by theories of norm-setting and practice-based learning, it examines the relationship of the amount of provided and received feedback on one assignment to improvements in the quality of feedback on the next assignment. Data on peer feedback experiences and behaviors across multiple assignments were taken from students across two introductory level undergraduate courses (N = 360). Multi-regression analyses reveal that the number and length of feedback provided predicted growth in helpfulness rates, and both improvements in domain performance and the reviewer’s preference for length explains the effects on review helpfulness. Further, compared with high-performing students, low-performing students show more remarkable growth in helpfulness from providing feedback.



中文翻译:

通过同行反馈的经验学习提高同行反馈的质量

摘要

同行评审经常被认为是一种强大而有效的评估和反馈技术,但许多学生缺乏经验,有时难以提供有意义的反馈。最好的做法是为学生提供一些关于如何成为一名优秀审稿人的培训,但很少有班级能够在这种培训上投入大量时间,并且假设审阅质量会随着经验的增加而提高。本研究直接检验了在同行反馈活动中什么样的经历可以提高评论质量。特别是,通过规范制定和基于实践的学习理论组织,它检查了对一项作业提供和收到的反馈量与下一项作业反馈质量改进之间的关系。N  = 360)。多回归分析表明,反馈的数量和长度提供了有用率的预测增长,领域性能的改进和评论者对长度的偏好都解释了对评论有用性的影响。此外,与成绩优异的学生相比,成绩差的学生在提供反馈的帮助上表现出更显着的增长。

更新日期:2020-10-18
down
wechat
bug