FORMATIVE ASSESSMENT IN LANGUAGE EDUCATION POLICIES: EMERGING LESSONS FROM WALES AND SCOTLAND
Bibliographic record
Abstract
Formative assessment, particularly in the current form known as Assessment for Learning (AfL), has caught the attention of policymakers in many education jurisdictions. Diverse educational systems such as Hong Kong and Western Canada have publicly endorsed the principles and practice of AfL. In the United Kingdom, progressive devolution of state power from London has meant that Scotland and Wales now have national autonomy in education matters. In a dramatic reversal of policy, both of these “home” countries have in the past four years dismantled the heavily test-oriented schooling regime. Instead both the Welsh and Scottish administrations have adopted assessment policies that prioritize learning. This article discusses (1) the political and ideological trajectories that have supported the emergence of the for-learning assessment policies and (2) the fit (or lack of) between AfL principles and the prevailing espoused educational values in these two nations. The potential impact of these developments for assessment of English as an additional/second language (EAL) in schooling education will be discussed.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".