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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".