Putting research into practice: Applying evidence-based principles to foster student learning in statistics and data science
Bibliographic record
Abstract
How can we apply evidence from research to best foster learning in our students? Inspired by the conference theme, a panel discussion at the IASE 2023 Satellite Meeting gave insight into the practical application of research-based learning principles to the teaching of Statistics and Data Science. The discussion was guided by the eight principles described in Lovett et al. (2023). These principles are grounded in learning theory and pedagogy and backed by empirical evidence. Panellists shared illustrative examples of how the principles are enacted in their teaching and participants had the opportunity to reflect on their own strategies for supporting learning through following these principles.
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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 | 0.420 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".