Standardizing Facilitator Development for Exploring Computer Science Professional Development
Bibliographic record
Abstract
A key strategy for broadening CS participation in the Chicago Public Schools (CPS) has been the enactment of a high school CS graduation requirement. The Exploring Computer Science (ECS) curriculum and professional development (PD) program serve as a core foundation for supporting enactment of this policy. The CAFÉCS researcher-practitioner partnership provides support for ECS implementation in CPS. An important part of the sustainability of the ECS PD model in CPS is the development of local workshop facilitators. Potential facilitators have generally been selected based on the CAFÉCS team's personal familiarity with active ECS teachers. Once selected, teachers engage in a two-year apprenticeship program to become facilitators. However, in the three years since the enactment of the policy, the number of ECS teachers and students has grown significantly. This rapid expansion of the CS teaching force has strained the ability to confidently identify new facilitators from a large pool of teachers and ensure consistency of workshop implementation. As a result, CAFÉCS is exploring how to supplement the ECS Facilitator Development Model through a proactive recruitment model and explicit support for the mentoring process.
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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 | 0.179 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".