What matters for competent teaching? A multinational comparison of teaching practicum assessment rubrics
Bibliographic record
Abstract
Practicum assessment rubrics have a backwash effect on preservice teachers' learning through the criteria they transmit. This article presents a documentary analysis of ten rubrics used across six countries: South Africa, India, England, Singapore, Canada, and Sweden. We compare the dispositions, knowledge, outcomes, and reasoning. We use Legitimation Code Theory (LCT) to show how practicum assessments are legitimated differently. Some rubrics emphasise preservice teachers’ dispositions and whether they implement protocols correctly. Others emphasise their capacity for reasoning in context. These positions call for teacher educators and policymakers to interrogate where the emphasis is in their own assessments. • This paper presents a documentary analysis of ten teaching practicum assessment rubrics from six countries. • Our analysis reveals global trends in assessing the practicum and how contextual differences can manifest. • Some rubrics list many discrete criteria specifying knowledge, skills and dispositions that assessors should verify. • Others value preservice teachers' capacity to make appropriate pedagogic choices and deliver their lessons effectively. • Stakeholders are invited to analyse how their own practicum assessment rubrics support preservice teacher learning.
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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 | 0.032 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".