PROFESSIONAL PRACTICE ISSUES IN THE ASSESSMENT OF COGNITIVE FUNCTIONING FOR EDUCATIONAL APPLICATIONS
Bibliographic record
Abstract
Research has demonstrated that many children have learning problems related to deficits in specific cognitive processes that are not adequately represented by a single IQ score. The administration of cognitive measures that include narrow abilities is useful in understanding specific learning problems and developing effective interventions. However, school psychology training programs have not readily adopted contemporary assessment practices. This article reviews the historical and legislative factors influencing school psychologists’ use of intellectual measures for identifying children with learning and other high‐incidence disabilities. Distinctions between contemporary cognitive assessment and traditional IQ testing are reviewed. Specific challenges to incorporating evidence‐based assessment practice within school psychology training programs are identified. Guidelines for using alternative research‐based procedures that include the use of cognitive measures to assess a child's strengths and weaknesses are provided. Potential directions for the application of cognitive theory in educational settings, professional training in appropriate interpretive strategies, and ethical guidance for the appropriate use of cognitive measures are also 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 | 0.332 | 0.457 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".