Paired Teaching: A Professional Development Model for Adopting Evidence-Based Practices
Bibliographic record
Abstract
How can we best support instructors to learn, practice, and retain student-centered, active-learning teaching strategies in their undergraduate courses? While approaches like professional development workshops may inspire some, permanent adoption of new pedagogy is rare. Here, we investigate “paired teaching” to achieve adoption and continued use of evidence-based practices. In this model, an instructor with little or no experience in student-centered teaching is paired with an experienced instructor in a semester-long course that has established student-centered pedagogy. This study evaluates information from eight pairs of instructors over a three-year period. Data was collected before, during, and after the paired teaching semester through interviews, written reflections, and teaching observations. Results indicate that paired teaching is beneficial as a professional development model for new instructors who have little teaching experience. The teaching practice of these instructors evolved to be increasingly student-centered, and they continued to use this pedagogy in subsequent classes. More established instructors who were less familiar with active learning showed lower tendencies to incorporate new pedagogies into their existing classes. We suggest best practices to maximize benefits and mitigate challenges associated with paired teaching.
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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 | 0.051 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".