Defining and Evaluating Models of Cognition Used in Educational Measurement to Make Inferences About Examinees' Thinking Processes
Bibliographic record
Abstract
The purpose of this paper is to define and evaluate the categories of cognitive models underlying at least three types of educational tests. We argue that while all educational tests may be based—explicitly or implicitly—on a cognitive model, the categories of cognitive models underlying tests often range in their development and in the psychological evidence gathered to support their value. For researchers and practitioners, awareness of different cognitive models may facilitate the evaluation of educational measures for the purpose of generating diagnostic inferences, especially about examinees' thinking processes, including misconceptions, strengths, and/or abilities. We think a discussion of the types of cognitive models underlying educational measures is useful not only for taxonomic ends, but also for becoming increasingly aware of evidentiary claims in educational assessment and for promoting the explicit identification of cognitive models in test development. We begin our discussion by defining the term cognitive model in educational measurement. Next, we review and evaluate three categories of cognitive models that have been identified for educational testing purposes using examples from the literature. Finally, we highlight the practical implications of “blending” models for the purpose of improving educational measures .
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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 | 0.101 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".