2. Using Creativity and Collaboration to Develop Innovative Programs That Embrace Diversity in Higher Education
Bibliographic record
Abstract
This paper provides an example of an innovative solution to program development that addresses the diverse needs of teacher educators throughout various geographical locations in Florida, through a collaborative multi-university, muti-agency teacher training program funded by one collaborative grant. Innovation is driven out of need, and I will discuss how I identified the needs at my university and then utilized creativity and collaboration to network and obtain the grant, which then facilitated, developed, and taught in a new M.Ed. program in Arts and Academic Interdisciplinary Education. Program content and delivery were both planned around the diverse student population within the multi-university collaboration, with each university designing diverse programs to address the specific needs of their population but with the same concept of arts integration. Collaboration also occurred within each university: the College of Arts and Science and the College of Education. In addition, teachers were required to collaborate as coaches in their schools to train and support others in increasing arts integration in their schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".