Formative Assessment and Writing: A Meta-Analysis
Abstract
To determine whether formative writing assessments that are directly tied to everyday classroom teaching and learning enhance students’ writing performance, we conducted a meta-analysis of true and quasi-experiments conducted with students in grades 1 to 8. We found that feedback to students about writing from adults, peers, self, and computers statistically enhanced writing quality, yielding average weighted effect sizes of 0.87, 0.58, 0.62, and 0.38, respectively. We did not find, however, that teachers’ monitoring of students’ writing progress or implementation of the 6 + 1 Trait Writing model meaningfully enhanced students’ writing. The findings from this meta-analysis provide support for the use of formative writing assessments that provide feedback directly to students as part of everyday teaching and learning. We argue that such assessments should be used more frequently by teachers, and that they should play a stronger role in the Next-Generation Assessment Systems being developed by Smarter Balanced and PARCC.
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Feedback effectiveness depends on contextual factors (learning environment, learner characteristics) and intrinsic features (modality, delivery mode, source, content).
“Its impact depends on contextual factors such as the learning environment and learner characteristics (Heckler and Mikula, 2016), as well as intrinsic features of the feedback itself, including modality, delivery mode, source, and, most importantly, its content (Ramadan Elbaioumi Shaddad and Jember, 2024; Graham et al., 2015; Rüdian et al., 2025).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Graham et al., 2015
Graham et al. (2015) meta-analysis of true and quasi-experimental studies on formative assessment of writing in grades one to eight yielded average weighted effect sizes of 0.87 for teacher feedback, 0.58 for peer feedback, 0.62 for self feedback, and 0.38 for computer feedback.
“The metaanalysis by Graham et al. (2015), which examined true and quasi-experimental design studies on formative assessment of writing in grades one to eight, categorized the source of formative feedback in terms of teachers, peers, self, and computers, and yielded average weighted effect sizes of 0.87, 0.58, 0.62, and 0.38, respectively.”
From The Next Black Box of Formative Assessment: A Model of the Internal Mechanisms of Feedback Processing (2022) · cites this paper as Graham et al. (2015)
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Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
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Untitled work
Chan, C. K. Y., Lo, N., & Wong, G. (2024)
- Authors
- Graham, S., Hebert, M., & Harris, K. R.
- Affiliations
- Arizona State University (US)
- University of Nebraska–Lincoln (US)
- Venue
- The Elementary School Journal
- Publisher
- University of Chicago Press
- Published
- 2015-06
- Volume
- 115
- Issue
- 4
- Pages
- 523-547
- ISSN
- 0013-5984 (ISSN-L)
- 1554-8279
- OpenAlex
- W2262260726
- Cited by
- 340
- References
- 54
- Citations by year
- 2026: 31 · 2025: 44 · 2024: 53 · 2023: 45 · 2022: 30 · 2021: 36 · 2020: 21 · 2019: 18 · 2018: 36 · 2017: 13 · 2016: 7 · 2015: 6
- FWCI
- 103.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Elementary School Journal (publishedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Writing and Handwriting Education
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Keywords
- formative assessment, writing assessment, writing feedback, peer feedback, self-assessment, computer-based feedback, writing quality, elementary school students, PARCC
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