A Multiple Case Study of Teaching-Focused Professional Development Programs Offered at Three Different Types of US Institutions of Higher Education
Bibliographic record
Abstract
Teaching-focused professional development (PD) programs offered at institutions of higher education (IHEs) are uniquely positioned to be levers of change that improve the quality of undergraduate science, technology, engineering, and mathematics (STEM) education in ways that broaden participation in STEM education, workforce development, and career pathways in the United States (US). PD programs and their potential to transform undergraduate STEM education, however, are understudied. This multiple-case study compares suites of PD programs offered at three IHEs in the US: a community college, an emerging research institution, and a research-intensive university. Each suite of PD programs is characterized in terms of program structure, implementation, and potential to transform undergraduate STEM education. The presented results illustrate the existence of a wide range of ways in which PD programs are structured and implemented. A key finding is a suite of PD programs offered at these IHEs has greater potential to transform undergraduate STEM education when embedded in an institutional culture that highly prioritizes the teaching enterprise. Lastly, the results are synthesized into an innovative framework. The framework can be used as a tool to design, implement, and evaluate PD programs so they have greater potential to transform undergraduate STEM education in the US.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".