Collaborative Design of Professional Graduate Programs in Education
Bibliographic record
Abstract
Faculties of Education in North America are experiencing an increase in demand for professional graduate programs that provide flexible and accessible research pathways for working professionals. Our School of Education offers high quality professional graduate programs that increase access and respond directly to complex needs and problems of practice in education. We describe the design and design thinking approach our faculty collectively undertook to redesign our professional graduate programs. The design was guided by a commitment to research informed and research active learning experiences that enable professionals to develop expertise, draw upon evidence, and act with integrity as they lead innovation and change in educational organizations. The program design provides professionals with opportunities to complete their graduate program in both blended and online formats. Degree programs are cohort based, discipline focused, and coherently structured. Many of our specialized topics are developed in partnership with the professions we serve, and each of our graduate programs is grounded in current research and engages students in active research-based learning. Participatory, collaborative, and interdisciplinary learning experiences are characterized by signature pedagogies. Our professional graduate programs create scholars of the profession through strong connections with the disciplines, communities, and professions we serve. Results of the redesign include improved results in student satisfaction, time to completion, increased retention, and have yielded high completion rates. Design knowledge and insights gained after eight years of evaluation document the strength and quality of our graduates and an increased proportion of international students in all of our graduate program areas.
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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 | 0.032 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".