International comparative assessment of early learning in exceptional learners: Potential benefits, caveats, and challenges
Bibliographic record
Abstract
Over the decades, it is evident that exceptional learners have been excluded from participating in international assessments such as OECD’s PISA (Programme for International Student Assessment) due to their disabilities. Drawing on the interdisciplinary theories and perspectives of educational assessment, measurement, and early childhood special education, the paper discusses the potential benefits young children with special needs may gain from the International Early Learning and Child Well-being Study (IELS), as well as considering caveats and challenges accompanying the use of IELS for these young special education populations. In particular, it raises a range of questions about what and how to collect, validly interpret, and use the IELS data to enhance early learning and development of exceptional learners in participating countries. Finally, the paper discusses accommodations that promote inclusionary assessment practices and level the playing field for young children with special needs.
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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 | 0.164 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".