A structured approach to developing an introductory statistics course for graduate students: Using data to teach about data
Bibliographic record
Abstract
Background/Objective: It was identified in the largest graduate unit of the Faculty of Medicine of a major Canadian University that there was a critical unmet curricular need for an introductory statistics and study design course. Based on the collective findings of an external institute review, both quantitative and qualitative data were used to design, develop, implement, evaluate, and refine such a course. Methods: In response to the identified need and inherent challenges to streamlining curriculum development and instructional design in research-based graduate programs representing many biomedical disciplines, the institute used the analyze, design, develop, implement and evaluate instructional design model to guide the data-driven development and ongoing monitoring of a new study design and statistics course. Results: The results demonstrated that implementing recommendations from the first iteration of the course (Fall 2021) into the second iteration (Winter 2023) led to improved student learning experience (3.18/5 weighted average (Fall 2021) to 3.87/5 (Winter 2023)). In the second iteration of the course, a self-perceived statistics anxiety test was administered, showing a reduction in statistics anxiety levels after completing the course (2.41/4 weighted average before the course to 1.65/4 after the course). Conclusion: Our experiences serve as a valuable resource for educators seeking to implement similar improvement approaches in their educational settings. Furthermore, our findings offer insights into tailoring course development and teaching strategies to optimize student learning.
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.
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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".