ERIC ED532562: The OECD, PISA and the Impacts on Educational Policy
Bibliographic record
Abstract
Large-scale assessment regularly takes place in most jurisdictions across Canada, a fact not lost on the Fraser Institute and other right wing think tanks such as AIMS (Atlantic Institute for Market Studies) which use the test results as the primary basis for compiling school rankings at both the elementary and secondary level (see Gutstein, 2010). The frequency of external testing at different levels (provincial/territorial, national, international)--coupled with the high visibility accorded by the mainstream media to the results, usually in the form of league tables, and the imperatives of short term political mandates--have all contributed to a focus on improving one's position within the list of rankings, as well as to a narrow focus on the tested subjects--math, science, reading. In this era of accountability-by-numbers, the elevated status accorded to large-scale external assessments such as PISA (Programme for International Student Assessment) results is symptomatic of a trend towards data-driven policy initiatives in education, and the need for regular sources of outcome data to constantly feed narrow indicators of accountability. The dilemma facing teachers and teaching, a profession "typically driven by ethical motive or intrinsic desire," is that it is caught between two competing forces in schools: "Teachers try to balance their work between the moral purpose of student-centred pedagogy within education as a public good, on one hand, and the drive for higher standards through perceived efficiency of the presentation-recitation mode of instruction and the perspective of education as a private good." This publication discusses the following topics: (1) Organisation for Economic Co-operation and Development (OECD)--education indicators and international surveys; (2) PISA's impact on education policy; (3) PISA criticisms at a glance; and (4) Some observations on PISA's influence in Canada. (Contains 3 endnotes and 40 references and sources.)
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.004 |
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; both teacher heads agree on what is shown here.
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".