Verbal Reports as Data for Cognitive Diagnostic Assessment
Bibliographic record
Abstract
The term cognitive diagnostic assessment (CDA) is used in this chapter to refer to a specific type of student evaluation. Unlike classroom-based tests designed by teachers or large-scale assessments designed by test developers to measure how much an examinee knows about a subject domain, CDAs are designed to measure the specific knowledge structures (e.g., distributive rule in mathematics) and processing skills (e.g., applying the distributive rule in appropriate mathematical contexts) an examinee has acquired. The type of information provided by results from a CDA should answer questions such as the following: Does the examinee know the content material well? Does the examinee have any misconceptions? Does the examinee show strengths for some knowledge and skills but not others? The objective of CDAs, then, is to inform stakeholders of examinees' learning by pinpointing the location where the examinee might have specific problem-solving weaknesses that could lead to difficulties in learning. To serve this objective, CDAs are normally informed by empirical investigations of how examinees understand, conceptualize, reason, and solve problems in content domains (Frederiksen, Glaser, Lesgold, & Shafto, 1990; Nichols, 1994; Nichols, Chipman, & Brennan, 1995). In this chapter, we focus on two methods for making sense of empirical investigations of how examinees understand, conceptualize, reason, and solve problems in content domains. As a way of introduction, we first briefly discuss the importance of CDAs for providing information about examinees' strengths and weaknesses, including the ways in which CDAs differ from traditional classroom-based tests and large-scale tests.
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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 | 0.013 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.019 |
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".