Does matching peers at finer-grained levels of prior performance enhance gains in task performance from peer review?
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| Title: | Does matching peers at finer-grained levels of prior performance enhance gains in task performance from peer review? |
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| Authors: | Zong, Zheng1 (AUTHOR) zongzheng@cupl.edu.cn, Schunn, Christian D.2 (AUTHOR) |
| Source: | International Journal of Computer-Supported Collaborative Learning. Sep2023, Vol. 18 Issue 3, p425-456. 32p. |
| Subject Terms: | *Student assignments, Task performance, Peers |
| Abstract: | Online peer feedback has proven to be practically useful for instructors and to be useful for learning, especially for the feedback provider. Because students can vary widely in skill level, some research has explored matching reviewer and author by performance level. However, past research on the impacts of reviewer matching has found little effect but used a simple binary high–low approach, which may mask the relative benefits of performance matching. In the current study, we leveraged a large dataset involving three large biology courses implementing multiple assignments with online peer feedback. This large dataset enabled dividing students into four levels of relative task performance to tease apart the relative contributions of providing and receiving feedback within the 16 different author–reviewer performance pairings. The results reveal that changes in task performance over assignments attributable to reviewing experiences vary by these finer-grained prior performance distinctions. In particular, providing to students at the same performance level appears to be beneficial, and receiving feedback from students at the same level is helpful except for very low-performing students. A simulation was used to examine the combined effects of receiving and providing under different algorithms for assigning reviewers to documents. The simulations suggest a matching algorithm will produce overall better outcomes than a random assignment algorithm for students at each of the four performance levels. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer-Supported Collaborative Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Online peer feedback has proven to be practically useful for instructors and to be useful for learning, especially for the feedback provider. Because students can vary widely in skill level, some research has explored matching reviewer and author by performance level. However, past research on the impacts of reviewer matching has found little effect but used a simple binary high–low approach, which may mask the relative benefits of performance matching. In the current study, we leveraged a large dataset involving three large biology courses implementing multiple assignments with online peer feedback. This large dataset enabled dividing students into four levels of relative task performance to tease apart the relative contributions of providing and receiving feedback within the 16 different author–reviewer performance pairings. The results reveal that changes in task performance over assignments attributable to reviewing experiences vary by these finer-grained prior performance distinctions. In particular, providing to students at the same performance level appears to be beneficial, and receiving feedback from students at the same level is helpful except for very low-performing students. A simulation was used to examine the combined effects of receiving and providing under different algorithms for assigning reviewers to documents. The simulations suggest a matching algorithm will produce overall better outcomes than a random assignment algorithm for students at each of the four performance levels. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15561607 |
| DOI: | 10.1007/s11412-023-09401-4 |
