Implementing Coteaching and Cogenerative Dialoguing in Urban Science Education
Bibliographic record
Abstract
Over the past 7 years the authors have been involved in the development of a new model for the education of science teachers that has the potential to address teacher education in challenging urban settings characterized by problems such as teacher turnover and retention, low job satisfaction, and contradictions arising from cultural and ethnic diversity. An intensive research program accompanied the development effort; the research results were used as resources in redesigning the evolving model to make it more appropriate for the situations at hand. The science teacher education program at an urban university was built around a yearlong field experience, during which all prospective teachers learned to teach in an urban high school while coteaching, that is, while teaching at the elbow of a mentor teacher or one or more peers. Over this period, a number of different configurations of coteaching and the associated cogenerative dialoguing were tried, tested, and investigated. The paper describes the historical development of the different configurations of the model and the emergent contradictions that led the researchers to enact changes to their approach. The central idea in the development effort was the creation of an environment that (a) best affords the learning of how to teach in urban high schools, (b) decreases teacher isolation, (c) mitigates turnover and retention, and (d) addresses contradictions arising from the cultural and ethnic diversity of students and teachers. Most importantly, this model of teacher education and enhancement simultaneously multiplies the resources and opportunities to support the learning of students.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".