Moving beyond Country Rankings in International Assessments: The Case of PISA
Bibliographic record
Abstract
The Programme for International Student Assessment (PISA) was designed by the Organisation for Economic Cooperation and Development (OECD) to evaluate the quality, equity, and efficiency of school systems around the world. Specifically, the PISA has assessed 15-year-old students’ reading, mathematics, and science literacy on a 3-year cycle, since 2000. Also, the PISA collects information about how those outcomes are related to key demographic, social, economic, and educational variables. However, the preponderance of reports involving PISA data focus on achievement variables and cross-national comparisons of achievement variables. Challenges in evaluating achievement of students from different cultural and educational settings and data concerning students’ approaches to learning, motivation for learning, and opportunities for learning are rarely reported. A main goal of this themed issue of Teachers College Record (TCR) is to move the conversation about PISA data beyond achievement to also include factors that affect achievement (e.g., SES, home environment, strategy use). Also we asked authors to consider how international assessment data can be used for improving learning and education and what appropriate versus inappropriate inferences can be made from the data. In this introduction, we synthesize the six articles in this issue and themes that cut across them. Also we examine challenges associated with using data from international assessments, like the PISA, to inform education policy and practice within and across countries. We conclude with recommendations for collecting and using data from international assessments to inform research, policy, and teaching and 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 | 0.123 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.041 | 0.056 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".