Avoiding Misconception, Misuse, and Missed Opportunities: The Collection of Verbal Reports in Educational Achievement Testing
Bibliographic record
Abstract
The collection of verbal reports is one way in which cognitive and developmental psychologists gather data to formulate and corroborate models of problem solving. The current use of verbal reports to design and validate educational assessments reflects the growing trend to fuse cognitive psychological research and educational measurement. However, doubts about the trustworthiness or accuracy of verbal reports may suggest a potential reversal of this trend. Misconceptions about the trustworthiness of verbal reports could signal misuse of verbal reports and, consequently, waning interest and missed opportunities in the description of cognitive models of test performance. In this article, misconceptions of verbal reports are addressed by (a) discussing the value of cognitive models for educational achievement testing; (b) addressing pertinent issues in the collection of verbal reports from students; and (c) concluding with avenues for a more productive union between cognitive psychological research and educational measurement.
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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 | 0.509 | 0.747 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.071 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier 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".