Programmatic assessment for learning: A programmatically designed assessment for the purpose of learning: AMEE Guide No. 174

Programmatic assessment for learning (PAL) involves programmatically structured collection of assessment data for the purpose of learning. In this guide, we examine and provide recommendations on several aspects: First, we review the evolution that has led to the development of programmatic assessment, providing clarification of some of its terminology. Second, we outline the learning processes that guide the design of PAL, including distributed learning, interleaving, overlearning, and test-enhanced learning. Third, we review the evolving nature of validity and provide insights into validity from a program perspective. Finally, we examine opportunities, challenges, and future directions of assessment in the context of artificial intelligence.

Authors

Institutions

Publication Details

Journal
Medical Teacher
Published
2024-10-05
DOI
https://doi.org/10.1080/0142159x.2024.2409936
Citations
19
Primary Topic
Innovations in Medical Education
Type
article
Field-Weighted Citation Impact
10.32
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Programmatic assessment for learning: A programmatically designed assessment for the purpose of learning: AMEE Guide No. 174

Lambert Schuwirth, Dario M. Torre
19 citations
Medical Teacher
Innovations in Medical Education
10.32
article

Programmatic assessment for learning: A programmatically designed assessment for the purpose of learning: AMEE Guide No. 174

Lambert Schuwirth, Dario M. Torre
article en
19 citations

Abstract

Programmatic assessment for learning (PAL) involves programmatically structured collection of assessment data for the purpose of learning. In this guide, we examine and provide recommendations on several aspects: First, we review the evolution that has led to the development of programmatic assessment, providing clarification of some of its terminology. Second, we outline the learning processes that guide the design of PAL, including distributed learning, interleaving, overlearning, and test-enhanced learning. Third, we review the evolving nature of validity and provide insights into validity from a program perspective. Finally, we examine opportunities, challenges, and future directions of assessment in the context of artificial intelligence.

Medical TeacherVol. 47(6)
University of Central Florida (US), Flinders University (AU), Florida College (US)
Quality Education
Openalex Percentile: Top 1%
Innovations in Medical Education
10.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.