Teaching teachers: what [should] teacher educators “know” and “do” and how and why it matters
Bibliographic record
Abstract
Research conducted internationally an d nationally reveals a persistent finding: early career teachers feel under-prepared to work effectively with the full range of learners who comprise the contemporary school classroom. The National College for Teaching and Leadership survey of Newly Qualified Teachers (NQTs) (NCTL, 2015 ) revealed that UK graduates felt ill-prepared to meet “the needs of pupils from all ethnic back- grounds and those for whom English is an additional language” (pp. 88 – 89). Similarly, teachers in Canada contributing to The State of Educators ’ professional Learning in Canada identified “working with all students in an inclusive environment” ; “supporting diverse learner needs” , “social issues (e.g., poverty) ” and “equity and poverty education” as priorities for professional development (Campbell, et al., 2016 ,p.29). Furthermore, most recently in Australia, graduate teachers have reported feeling less than prepared when it comes to teaching students from culturally, linguistically and economically diverse backgrounds, students with a disability and those from Aboriginal and Torres Strait Islander families (Mayer et al., 2017 ;Rowan,Kline,&Mayer, 2017 )...
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".