Designing and Implementing a Competency‐Based Formative Progress Assessment System at a Canadian Dental School
Bibliographic record
Abstract
Progress testing is an innovative formative assessment practice that has been found successful in many educational programs. In progress testing, one exam is given to students at regular intervals as they progress through a curriculum, allowing them to benchmark their increase in knowledge over time. The aim of this study was to assess the first two years of results of a progress testing system implemented in a Canadian dental school. This was the first time in North America a dental school had introduced progress testing. Each test form contains 200 multiple-choice questions (MCQs) to assess the cognitive knowledge base that a competent dentist should have by the end of the program. All dental students are required to complete the test in three hours. In the first three administrations, three test forms with 86 common items were administered to all DMD students. The total of 383 MCQs spanning nine domains of cognitive knowledge in dentistry were distributed among these three test forms. Each student received a test form different from the previous one in the subsequent two semesters. In the fourth administration, 299 new questions were introduced to create two test forms sharing 101 questions. Each administration occurred at the beginning of a semester. All students received individualized reports comparing their performance with their class median in each of the domains. Aggregated results from each administration were provided to the faculty. Based on analysis of students' responses to the common items in the first two administrations, progression in all domains was observed. Comparing equated results across the four administrations also showed progress. This experience suggests that introducing a progress testing assessment system for competency-based dental education has many merits. Challenges and lessons learned with this assessment are discussed.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".