Teachers and Teacher Candidates Learning Together: Reported Ideas Regarding a Co-Teaching Model
Bibliographic record
Abstract
A recurring issue in science teacher education is how to support teachers using student-centred scientific inquiry projects. This study investigates the first year of a three-year project (Inquire Together) that aims to support teacher candidate/teacher mentor teams using student-centred scientific inquiry projects while co-teaching during practicum. Co-teaching involves two or more teachers working together, co-planning, co-instructing and co-reflecting, for the benefit of students and to support each other’s development as teachers. The steps in the first year of the Inquiry Together project involved the following: a) developing a co-teaching model for our practicum in a two-day workshop, follow up focus groups and interviews; b) modelling student-centred scientific inquiry opportunities for teacher candidates and teachers; and c) supporting two teacher candidate/teacher mentor co-teaching teams implementing this pedagogy with practicum classes. The Covid-19 pandemic led to co-teaching teams moving to online teaching part-way through practicum, and students continuing their projects at home. We used a case study approach to investigate the affordances and constraints of Inquire Together for supporting teachers using student-centred scientific inquiry projects. Data included video and audio recordings of the workshop, focus groups, interviews, co-teacher planning meetings, high school classes and co-teacher reflection meetings, as well as co-teachers’ unit and lesson plans and online communication. Our initial findings from thematic analysis indicate the importance of the following five elements: time together for co-teaching teams outside the classroom (for planning and reflection); co-teachers developing mutual understanding; making equity between co-teachers visible for students; communication; and both teachers’ engagement in all activities.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| 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".