Assessment steers learning down the right road: Impact of progress testing on licensing examination performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although it is generally accepted that assessment steers learning, this is generally viewed as an undesirable side effect. Recent evidence suggests otherwise. Experimental studies have shown that periodic formative assessments can enhance learning over equivalent time spent in study (Roediger & Karpicke 2006). However, positive effects of assessment at a curriculum level have not been demonstrated. Progress tests are a periodic formative assessment designed to enhance learning by providing objective and cumulative feedback, and by identifying a subgroup of students who require additional remediation. McMaster adopted the progress test methods in 1992-1993, as a consequence of poor performance on a national licensing examination. This article shows the positive effect of this innovation, which amounts to an immediate increase of about one-half standard deviation in examination scores, and a consistent upward trend in performance. The immediate effect of introducing objective tests was a reduction in failure rate on the licensing examination from 19% to 4.5%. Various reasons for this improvement in performance are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it