CoChat
Sign Up
Cited by 275EFL/ESL Teaching and LearningStudent Assessment and FeedbackEnglish Language Learning and Teaching

The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.

Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement

Link, Stephanie, Mehrzad, Mohaddeseh, Rahimi, Mohammad·2022·Computer Assisted Language Learning
DOI 10.1080/09588221.2020.1743323
Open this paper in CoChat — free account

Abstract

Recent years have witnessed an increasing interest in the use of automated writing evaluation (AWE) in second language writing classrooms. This increase is partially due to the belief that AWE can assist teachers by allowing them to devote more feedback to higher-level (HL) writing skills, such as content and organization, while the technology addresses lower-level (LL) skills, such as grammar. As is speculated, student revisions will then be positively impacted. However, little evidence has supported these claims, calling into question the impact of AWE on teaching and learning. The current study explored these claims by comparing two second language writing classes that were assigned to either an AWE + teacher feedback condition or a teacher-only-feedback condition. Findings suggest that using AWE as a complement to teacher feedback did not have a significant impact on the amount of HL teacher feedback, but the teacher who did not use AWE tended to provide a greater amount of LL feedback than AWE alone. Furthermore, students seemed to revise the teacher’s LL feedback more frequently than LL feedback from the computer. Interestingly, students retained their improvement in accuracy in the long-term when they had access to AWE, but students who did not have access appeared to have lower retention. We explain the relevance of our findings in relation to an argument-based validation framework to align our work with state-of-the-art research in the field and contribute to a broader discussion about how AWE can be best provided to support second language writing development.

Overview

Integrity signals

Registry

Crossref · 10.1080/09588221.2020.1743323

Cites

66 of 66 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 66 references

Research works printed but not linked: 13

  1. Attali Y., 2004, Paper presented at the National Council on Measurement in Education (NCME)
  2. Chen C., 2008, Language Learning & Technology, 12, 94
  3. Cotos E., 2012, Technology across writing contexts and tasks, CALICO Monograph Series, 10, 81
  4. Cotos E., 2017, Special Issue in Language Learning and Technology, 21, 104
  5. Grimes D., 2010, The Journal of Technology, Language, and Assessment, 8
  6. Karim K., 2018, Language Teaching Research
  7. Lee J., 2012, A hybrid use of Criterion® and teacher feedback in process writing.
  8. Plakans L., 2019, Language Testing, 36 (2), 161–179
  9. Quick Placement Test. (2001). Oxford: Oxford University Press.
  10. Rahimi M., 2019, Language Teaching Research
  11. Victori, M. (1995). EFL writing knowledge and strategies: An interactive study (Unpublished PhD dissertation). Universitat Autonoma de Barcelona (Spain), Barcelona.
  12. Warschauer M., 2010, Language Learning & Technology, 14, 3
  13. Yoon H.-J., 2016, TESOL Quarterly, 51

Cited by

Sorted by citations

Items 201–275 of 275

Related

See all

Metadata

Authors
Link, S., Mehrzad, M., & Rahimi, M.
Affiliations
  • Oklahoma State University (US)
  • Shiraz University (IR)
Corresponding author
Rahimi, Mohammad
ORCID
Venue
Computer Assisted Language Learning
Publisher
Informa UK Limited
Published
2020-03-26
Volume
35
Issue
4
Pages
605-634
ISSN
  • 0958-8221 (ISSN-L)
  • 1744-3210
DOI
10.1080/09588221.2020.1743323
OpenAlex
W3013848609
Cited by
275
References
66
Citations by year
2026: 52 · 2025: 65 · 2024: 60 · 2023: 51 · 2022: 31 · 2021: 12 · 2020: 4
FWCI
114.15
Type
journal article
Language
en
Open access
closed
Locations
Computer Assisted Language Learning (publishedVersion)
Topics
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
  • Social Sciences › Education › Student Assessment and Feedback
  • Social Sciences › Education › English Language Learning and Teaching
Keywords
automated writing evaluation, teacher feedback, student revision, second language writing, writing accuracy, grammar feedback, writing development

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 ›

Are you an author, or do you see a problem with this page? Request a review