Microteaching: An Introspective Case Study with Middle School Teachers in New York City Public Schools
Bibliographic record
Abstract
The purpose of this study was to investigate the potential impacts of microteaching on experienced teachersparticipating in the Community Enterprise for Restoration Science (CCERS) Teaching Fellowship at Pace Universityas part of a National Science Foundation-funded research project on the education model known as the Curriculum andCommunity Enterprise for Restoration Science (CCERS). The program builds a learning community of teachers in thefellowship program as they participated in monthly workshops in cohorts and continuously interact with each otherduring the two years of the program. Each teacher in Cohort 1 of the CCERS Fellowship was required to provide a brieflesson that they have used in the classrooms from the CCERS curriculum. Generally, the Teaching Fellows’micro-lessons contained appropriate objectives presented to the class aligned well to the objectives of the CCERSinitiative, which focused on harbor restoration learning within a STEM context. By conducting field studies atrestoration stations that students set up near their schools, students across all schools learned about the biology,chemistry, ecology and history of the Hudson River. In addition to teaching science content, all teachers incorporatedlessons on helping students to develop literacy strategies to build vocabulary. The microteaching modules allowed forteachers to gain insight as to how the curriculum was being implemented into other teachers’ classrooms. It permittedfor teachers’ exposure to the various teaching methods and resources being used to assist underrepresented studentsand students where English is a second language.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".