Redesign of a First-Year Theory Course Sequence in Biostatistics
Bibliographic record
Abstract
This communication describes the process and results of a curriculum review of a first-year sequence of courses in statistical inference within a Master of Biostatistics program. Our primary aim was to develop an innovative course in statistical theory that meets the needs of a diverse student audience, the majority of whom are seeking a terminal master’s degree while a minority will pursue PhD training in biostatistics. The main results were (1) different course paths for job-bound and PhD-bound students; and (2) the development of an innovative first course in statistical inference, which is a computationally-aided self-discovery of a salient (albeit not comprehensive) set of key concepts and techniques pertaining to statistical inference. The redesign process addressed a key conceptual barrier: namely, the unexamined assumption that deductive proofs are a necessary condition for rigorous presentation. Consistent with the principles of constructivism, we navigated this barrier by redefining the task to which pedagogic rigor should be applied: namely, to help students to develop a sound mental map of statistical inference. We believe that the approach we used to accomplish this redefined task could be generalized to additional aspects of statistical education, among others.
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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 | 0.086 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".