International trends in the implementation of assessment for learning: Implications for policy and practice
Abstract
This paper discusses the emergence of assessment for learning (AfL) across the globe with particular attention given to Western educational jurisdictions. Authors from Australia, Canada, Ireland, Israel, New Zealand, Norway, and the USA explain the genesis of AfL, its evolution and impact on school systems, and discuss current trends in policy directions for AfL within their respective countries. The authors also discuss the implications of these various shifts and the ongoing tensions that exist between A fL and summative forms of assessment within national policy initiatives.
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Formative assessment implementation remains stubbornly tricky in both high- and low-stakes assessment cultures due to the influence of high-stakes testing.
“First, even though formative assessment has been advocated for in educational research, policy and practice for decades, its implementation remains stubbornly tricky in both high- and low- stakes assessment cultures due to the influence of high- stakes testing (Birenbaum et al., 2015; Stobart, 2008; Volante et al., 2020).”
From Diversity of assessment practices and student perceptions of assessment in Finland (2025) · cites this paper as Birenbaum et al., 2015
The dominance of examinations has narrowed assessment menus on a global scale.
“The 'dominance of examinations' (Richardson, 2022, p. 22) in particular has been shown to narrow assessment menus on a global scale (see Barnes et al., 2000; Birenbaum et al., 2015; Volante et al., 2020).”
From Diversity of assessment practices and student perceptions of assessment in Finland (2025) · cites this paper as Birenbaum et al., 2015
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Enhancing learning with inspectable student models: Worth the effort?
Tacoma, S., Geurts, C., Slof, B., Jeuring, J. T., & Drijvers, P. H. M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106276 · Cited by 11 · see citing works
Automated intelligent feedback in university statistics education
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Implementing school-based assessment reforms to enhance student learning: a systematic review
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Computing Education Research Today
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Student partnership in assessment: What works, for whom, why, and under what circumstances?
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Student Knowledge and Misconceptions
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- Authors
- Birenbaum, M., DeLuca, C., Earl, L., Heritage, M., Klenowski, V., Looney, A., Smith, K., Timperley, H., Volante, L., & Wyatt-Smith, C.
- Affiliations
- Tel Aviv University (IL)
- Queen's University (CA)
- Lorna Earl and Associates, Canada
- University of California, Los Angeles (US)
- Queensland University of Technology (AU)
- National Council for Curriculum and Assessment, Ireland
- Nowegian University of Technology and Science and second University of Berge, Norway
- University of Auckland (NZ)
- Brock University (CA)
- Australian Catholic University, Australia
- Corresponding author
- Volante, Louis
- Venue
- Policy Futures in Education
- Publisher
- SAGE Publications
- Published
- 2015-01
- Volume
- 13
- Issue
- 1
- Pages
- 117-140
- ISSN
- 1478-2103 (ISSN-L)
- OpenAlex
- W2108604890
- Cited by
- 182
- References
- 49
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 16 · 2023: 19 · 2022: 17 · 2021: 29 · 2020: 12 · 2019: 46 · 2018: 12 · 2017: 6 · 2016: 3 · 2015: 3
- FWCI
- 39.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Policy Futures in Education (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Social Sciences › Political Science and International Relations › Global Educational Policies and Reforms
- Keywords
- assessment for learning, education policy, summative assessment, formative assessment, cross-national comparison, Western education systems, educational reform, assessment policy
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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