The effect of peer assessment on project performance of students at different learning levels
Abstract
Peer assessment has been increasingly integrated in educational settings as a strategy to foster student learning. Yet little has been studied about how students at different learning levels may benefit from peer assessment. This study examined how peer-assessment and students’ learning levels influenced students’ project performance using a two-way factorial design. One hundred and thirty teacher education students participated in this quasi-experimental study. When working on a technology-integrated lesson plan project, the experimental group completed an online peer assessment process while the control group followed the discussion method. Students’ learning levels were measured and divided into low, average and high achieving according to the quality of their draft lesson plans. Data analysis suggested that the impact of peer assessment on students’ lesson plan project seemed to vary according to students’ learning levels. While low- and average-achieving students showed significantly improved performance right after the integration of a peer assessment model, the model seemed to have had less impact on the performance of high-achieving students. Significance, implications and limitations of findings are discussed.
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Several studies have shown that peer assessment can effectively be carried out online.
“Several studies have shown that peer assessment can effectively be carried out online (e.g., Hsu 2016; Li and Gao 2016).”
From The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies (2020) · cites this paper as Li and Gao 2016
Peer assessment benefits include making the range of feedback wider and encouraging students to reflect, reducing marking load on teachers and increasing students' self-evaluation skills, and enhancing students' retention rate through motivation and reflection of their own learning.
“Numerous benefits are observed such as (1) making the range feedback wider and encourages students to reflect, (2) reduces marking load on teachers, and increases students' selfevaluation skills [17,51], and (3) enhances students retention rate through motivation and reflection of their own learning [56].”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [56]
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- Authors
- Li, L., & Gao, F.
- Affiliations
- Bowling Green State University (US)
- Corresponding author
- Li, Lan
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2015-06-08
- Volume
- 41
- Issue
- 6
- Pages
- 885-900
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W1534544955
- Cited by
- 79
- References
- 45
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 15 · 2022: 13 · 2021: 7 · 2020: 7 · 2019: 11 · 2018: 5 · 2017: 4 · 2016: 1
- FWCI
- 16.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- ScholarWorks@BGSU (Bowling Green State University) (submittedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Evaluation of Teaching Practices
- Keywords
- peer assessment, learning levels, project performance, pre-service teachers, online peer assessment, achievement differences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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