Validity evidence for programmatic assessment in competency-based education
Bibliographic record
Abstract
INTRODUCTION: Competency-based education (CBE) is now pervasive in health professions education. A foundational principle of CBE is to assess and identify the progression of competency development in students over time. It has been argued that a programmatic approach to assessment in CBE maximizes student learning. The aim of this study is to investigate if programmatic assessment, i. e., a system of assessment, can be used within a CBE framework to track progression of student learning within and across competencies over time. METHODS: Three workplace-based assessment methods were used to measure the same seven competency domains. We performed a retrospective quantitative analysis of 327,974 assessment data points from 16,575 completed assessment forms from 962 students over 124 weeks using both descriptive (visualization) and modelling (inferential) analyses. This included multilevel random coefficient modelling and generalizability theory. RESULTS: Random coefficient modelling indicated that variance due to differences in inter-student performance was highest (40%). The reliability coefficients of scores from assessment methods ranged from 0.86 to 0.90. Method and competency variance components were in the small-to-moderate range. DISCUSSION: The current validation evidence provides cause for optimism regarding the explicit development and implementation of a program of assessment within CBE. The majority of the variance in scores appears to be student-related and reliable, supporting the psychometric properties as well as both formative and summative score applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.326 | 0.630 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".