Putting It All Together: Cognitive Models to Inform the Design and Development of Large-Scale Educational Assessment
Bibliographic record
Abstract
In this penultimate chapter, we summarize our impetus for identifying and evaluating diagrammatic cognitive models in reading, science, and mathematics and offer some conclusions about where we go from here. Borrowing the definition from Leighton and Gierl (2007a), a cognitive model was defined as a “simplified description of human problem solving on standardized educational tasks, which helps to characterize the knowledge and skills students at different levels of learning have acquired and to facilitate the explanation and prediction of students' performance” (p. 6). In Chapter 1, we indicated that large-scale educational tests, redesigned and redeveloped from cognitive models in the learning sciences, may offer enhanced information (test-based inferences) about student problem solving and thinking. This enhanced information may help remediate the relatively low test performance of many students, including U.S. students, who are struggling to learn and demonstrate knowledge in core domains. In Chapter 1, we also presented accepted knowledge and principles from the learning sciences about the nature of thinking, learning, and performance to set the stage for what may be required for redesigning and redeveloping large-scale assessments. Illustrative empirical studies in the field of educational measurement were described to demonstrate attempts at redesigning and redeveloping educational assessments based on the learning sciences. Chapters 2, 3, 4, and 5 presented our criteria for evaluating cognitive models and also offered examples of diagrammatic cognitive models in reading, science, and mathematics that have garnered substantial empirical support.
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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 | 0.030 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".