Student preparation for large-scale assessments: A comparative analysis
Bibliographic record
Abstract
[Extract] Sam Sellar Bob Lingard David Rutkowski Keita Takayama and The Organisation for Economic Cooperation and Development’s (OECD) Programme for International Student Assessment (PISA) has become arguably the most influential and successful international large-scale assessment (ILSA) and represents a major global investment in test development, administration, data collection and analysis. PISA assesses reading, mathematical and scientific literacy and is conducted with 15-year-olds in approximately seventy countries every three years. This chapter examines different approaches to preparing students to sit for PISA. We compare four national cases at different points in time: (1) Prince Edward Island, Canada in PISA 2003; Japan following PISA 2003; Scotland in PISA 2012; and Norway in PISA 2015. Approaches to preparation across the four cases span a continuum from close adherence to the test administration guidelines to structural reforms that increase alignment between curricula and what PISA assesses. We aim to answer the following question: what can...
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".