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Feedback loops and the longer-term: towards feedback spirals
Open accessAbstract
A key challenge for feedback practice involves promoting student uptake through the closing of feedback loops. This paper investigates feedback loops by using the concepts of single and double-loop learning to interrogate student responses to feedback. Single-loop learning tackles an identified problem or task, whereas double-loop learning additionally re-evaluates how the problem or task is approached. Evidence from a five-year longitudinal enquiry into four undergraduate students’ experiences of feedback is used to analyse feedback loops. Students reported a variety of experiences: failing to engage significantly with end-of-semester comments; short-term uptake within modules which had two assignments; and longer-term efforts at improving their learning strategies. A model of long-term student engagement with feedback is proposed, including single-loop feedback processes, double-loop feedback processes and unresolved learning puzzles. Whereas feedback loops are mainly focused on the shorter-term, it is suggested that feedback spirals represent an alternative way of analysing complex, iterative longer-term learning processes. Implications for practice focus on student self-regulation and the development of student feedback literacy.
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“e concept of feedback loops is discussed which shows a trajectory of how students engage themselves in the feedback process, reflect and make ongoing adjustments during their learning process to enhance learning strategies [78].”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [78]
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“Their preferences resonate with the position of Boud and Molloy (2013) that feedback should be designed so that there are opportunities for feedback loops to be closed.”
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Rahman, N., & McSweeney, J. M. (2026) · Teaching & Learning Inquiry The ISSOTL Journal · doi:10.20343/teachlearninqu.14.22
“Traditional SETs operate as one-way, summative exchanges, offering limited opportunities for real-time reflection, growth, or mutual engagement (Carless 2019; Boud et al. 2013; Nicol and Macfarlane-Dick 2006).”
Yang, X., Carless, D. R., & Lin, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6643238
Self-citationАзбель, А. А., & Ilyushin, L. S. (2026) · Science for Education Today · doi:10.15293/2658-6762.2602.08
AI-Based Automated Assessment System for Enhanced Learning Evaluation
Andi, A., Juwariyah, A., Parisu, C. Z. L., Rosnawati, R., Permatasari, S. J., & Sisi, L. (2026) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-032-19130-4_28
Cruickshank, V. J., Mellor, C., & Mainsbridge, C. (2026) · European Physical Education Review · doi:10.1177/1356336x261448796
Collaborative Leadership in Education, Healthcare, and Climate Sectors
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Metadata
- Authors
- Carless, D.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Carless, David
- ORCID
- Carless, David
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2018-11-14
- Volume
- 44
- Issue
- 5
- Pages
- 705-714
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W2901943084
- Cited by
- 328
- References
- 31
- Citations by year
- 2026: 56 · 2025: 46 · 2024: 55 · 2023: 49 · 2022: 45 · 2021: 40 · 2020: 22 · 2019: 15
- FWCI
- 62.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Evaluation of Teaching Practices
- Social Sciences › Education › Higher Education Practises and Engagement
- Keywords
- feedback loops, single-loop learning, double-loop learning, student engagement with feedback, student self-regulation, feedback literacy, learning strategies, undergraduate 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.
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