The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Twelve Tips for programmatic assessment
Abstract
Programmatic assessment is an integral approach to the design of an assessment program with the intent to optimise its learning function, its decision-making function and its curriculum quality-assurance function. Individual methods of assessment, purposefully chosen for their alignment with the curriculum outcomes and their information value for the learner, the teacher and the organisation, are seen as individual data points. The information value of these individual data points is maximised by giving feedback to the learner. There is a decoupling of assessment moment and decision moment. Intermediate and high-stakes decisions are based on multiple data points after a meaningful aggregation of information and supported by rigorous organisational procedures to ensure their dependability. Self-regulation of learning, through analysis of the assessment information and the attainment of the ensuing learning goals, is scaffolded by a mentoring system. Programmatic assessment-for-learning can be applied to any part of the training continuum, provided that the underlying learning conception is constructivist. This paper provides concrete recommendations for implementation of programmatic assessment.
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In the citing paper
“Gaining prominence in medical education, programmatic assessment shifts the focus from isolated assessment events to the strategic collection and integration of multiple low-stakes data points over time [16,27].”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [27]
In the citing paper
“Each piece of evidence, whether formative or summative, is deliberately gathered to support learner development and guide high-stakes judgments about competence.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [27]
In the citing paper
“Finally, they promote the longitudinal compilation of performance data to coach the learner's continuing development (assessment for learning) and to make evidence-based decisions about the learner's progress (assessment of learning).[11][12][13][14][15]”
From The R2C2 Model in Residency Education: How Does It Foster Coaching and Promote Feedback Use? (2018) · cites this paper as [13]
In the citing paper
“Foundationally, the principles of programmatic assessment and CBME direct our attention to actively engaging learners in their assessment, feedback, and development activities.9,13,15”
From The R2C2 Model in Residency Education: How Does It Foster Coaching and Promote Feedback Use? (2018) · cites this paper as [13]
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Metadata
- Authors
- Van Der Vleuten, C. P. M., Schuwirth, L. W. T., Driessen, E. W., Govaerts, M. J. B., & Heeneman, S.
- Affiliations
- Flinders University (AU)
- Maastricht University (NL)
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2014-11-20
- Volume
- 37
- Issue
- 7
- Pages
- 641-646
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W1738312775
- Cited by
- 333
- References
- 33
- Citations by year
- 2026: 32 · 2025: 21 · 2024: 29 · 2023: 42 · 2022: 26 · 2021: 47 · 2020: 33 · 2019: 32 · 2018: 30 · 2017: 22 · 2016: 10 · 2015: 5
- FWCI
- 12.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion)
- Topics
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Educational Assessment and Pedagogy
- Keywords
- programmatic assessment, assessment for learning, assessment design, feedback to learners, curriculum quality assurance, high-stakes decision making, information aggregation, student self-regulation, mentoring, constructivist learning, medical education
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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