The Curriculum and Community Enterprise for Restoration Science S.T.E.M. + C Professional Learning Model: Expansion and Enhancement
Bibliographic record
Abstract
Professional Development in the field of education has undergone several shifts in focus. Currently, teacher contentknowledge and the ability to disseminate this knowledge is the focus in professional learning communities. Theimportance of creating a thriving STEM workforce in the United States has been promoted for the last decade.Studies have shown that capturing students’ interest must occur before they enter high school, ideally in the middleschool years (Blotnicky, Franz-Odendaal, French, & Phillip, 2018). Teachers are the conduits for encouragingstudents to explore STEM-related career options. Student engagement is piqued when there is a strong real-worldconnection to the content being presented. Students find relevance through actual experience with the concepts andskills incorporated in projects that are community-based. The Curriculum and Community Enterprise for RestorationScience STEM + C Project is the marriage of these two components. The professional development of the New YorkCity middle school teachers involved in the CCERS STEM +C Project furnishes these educators with the tools tostimulate students’ interest by tackling a problem in their local community using STEM-related content andcomputational thinking. The hope is that authenticity of the learning experience will entice all students, especiallythose under-represented populations in the STEM workforce, to consider this as a viable career pathway. Theanalysis of this project is intended to highlight the significant inroads made and the value of self-reflection andre-design in strengthening the work as it continues.
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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 | 0.003 | 0.001 |
| 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".