Strategies for using feedback students bring to higher education
Abstract
This article explores strategies for acting on feedback that students bring to their higher education, by exploring student perceptions on the guidance they received from their teachers in school or college. Whilst assessment issues have been the subject of a range of research studies, it is the contention of this article that little research has been undertaken to identify the strategies that students possess for ‘using’ feedback. This small‐scale research study was stimulated by M.R. Weaver’s 2006 Weaver, M.R. 2006. Do students value feedback? Student perceptions of tutors’ written response. Assessment and Evaluation in Higher Education, 31(3): 379–394. [Taylor & Francis Online] , [Google Scholar] finding that only a quarter of students had received guidance on how to use feedback prior to university. This exploration identifies the guidance for using feedback that 350 students in Humanities brought to their degree course. Analysis of responses revealed the strategies that students had been introduced to, identifying nine indigenous categories. Whilst almost 40% of these students indicated they had received guidance for using feedback, scrutiny of responses showed that many of these students confused ‘actual feedback’ with ‘guidance on how to use’ feedback. The implications of such starting positions for students and staff are considered: questioning the adequacy of such a foundation for students to be able to make effective use of the feedback they will receive in their de…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Students tend to prefer longer feedback.
“similar to the findings of [11], students tend to prefer longer feedback”
From Comparing Student Preferences for AI-Generated and Peer-Generated Feedback in AI-driven Formative Peer Assessment (2025) · cites this paper as [11]
In the peer-generated feedback preference prediction model, there is a positive relationship between Feedback Length and peer-generated feedback preference.
“The positive relationship between Feedback Length and peer-generated feedback preference suggests that, similar to the findings of [11], students tend to prefer longer feedback.”
From Comparing Student Preferences for AI-Generated and Peer-Generated Feedback in AI-driven Formative Peer Assessment (2025) · cites this paper as [11]
Cites
Getting the Message across: The Problem of Communicating Assessment Feedback
Higgins, R., Hartley, P., & Skelton, A. (2001) · Teaching in Higher Education · doi:10.1080/13562510120045230 · Cited by 549 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Making Sense of Assessment Feedback in Higher Education
Evans, C. (2013) · Review of Educational Research · doi:10.3102/0034654312474350 · Cited by 1,143 · see citing works
Developing sustainable feedback practices
Carless, D., Salter, D., Yang, M., & Lam, J. (2011) · Studies in Higher Education · doi:10.1080/03075071003642449 · Cited by 869 · see citing works
Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017) · Educational Psychologist · doi:10.1080/00461520.2016.1207538 · Cited by 884 · see citing works
Automatic feedback in online learning environments: A systematic literature review
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100027 · Cited by 440 · see citing works
Winstone, N. E., Nash, R. A., Rowntree, J., & Parker, M. (2017) · Studies in Higher Education · doi:10.1080/03075079.2015.1130032 · Cited by 417 · see citing works
Facilitating productive use of feedback in higher education
Jonsson, A. (2013) · Active Learning in Higher Education · doi:10.1177/1469787412467125 · Cited by 414 · see citing works
Feedback on feedback practice: perceptions of students and academics
Mulliner, E., & Tucker, M. (2017) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2015.1103365 · Cited by 261 · see citing works
Adcroft, A. (2011) · Higher Education Research & Development · doi:10.1080/07294360.2010.526096 · Cited by 121 · see citing works
Instructors’ Perspectives of Challenges and Barriers to Providing Effective Feedback
Paris, B. (2022) · Teaching and Learning Inquiry · doi:10.20343/teachlearninqu.10.3 · Cited by 33 · see citing works
Shin, I., Hwang, S. B., Yoo, Y. J., Bae, S., & Kim, R. Y. (2025) · Proceedings of the 15th International Learning Analytics and Knowledge Conference · doi:10.1145/3706468.3706488 · Cited by 16 · see citing works
“The positive relationship between Feedback Length and peer-generated feedback preference suggests that, similar to the findings of [11], students tend to prefer longer feedback.”
- Authors
- Burke, D.
- Affiliations
- University of Wolverhampton (GB)
- Corresponding author
- Burke, Deirdre
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2009-02
- Volume
- 34
- Issue
- 1
- Pages
- 41-50
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W2081684561
- Cited by
- 142
- References
- 1
- Citations by year
- 2025: 9 · 2024: 8 · 2023: 10 · 2022: 5 · 2021: 10 · 2020: 14 · 2019: 14 · 2018: 7 · 2017: 5 · 2016: 8 · 2015: 8 · 2014: 6 · 2013: 11 · 2012: 12
- FWCI
- 21.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Evaluation of Teaching Practices
- Social Sciences › Education › Higher Education Practises and Engagement
- Keywords
- student feedback, student perceptions, transition to higher education, teacher feedback, written feedback, student learning strategies, humanities students
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
