Evolution of a Qualifying Examination from a Timed Closed-Book Format to an Open-Book Collaborative Take-Home Format: A Case Study and Commentary
Bibliographic record
Abstract
Objective: Our master's program in biostatistics requires a qualifying examination (QE). A curriculum review led us to question whether to replace a closed-book format with an open-book one. Our goal was to improve the QE. Methods: This is a case study and commentary, where we describe the evolution of the QE, both in its goals and its content. The result was a week-long, open-book, collaborative, take-home examination structured around the analysis of two types of studies commonly encountered in biostatistical practice. Our evaluation of the revised format includes its fairness, student performance, and student feedback. Results: The new format has a number of advantages: (1) it has a specific educational goal; (2) it provides sufficient time for students to produce their best work; (3) it encourages students to review elements of the first-year curriculum as needed; and (4) it can be administered remotely, even during a pandemic. Potential concerns pertaining to cheating and rigor can be adequately addressed. The results of our evaluation of the examination have been encouraging. The QE is intended to be a "fair" examination that covers important material which is beneficial to students, and does so in a way that is transparent and puts everyone in a position to perform their best work. Conclusions: An examination using this format has much to recommend it. When designing an examination, it is important to (a) match its format with clearly specified educational goals; and (b) distinguish between the distinct constructs of difficulty and rigor.
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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 | 0.035 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".