Developing a Classification Tool Based on Bloom’s Taxonomy to Assess the Cognitive Level of Short Essay Questions
Why this work is in the frame
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Bibliographic record
Abstract
The cognitive level of short essay questions taken from assessments of two veterinary courses at the Faculty of Veterinary Medicine of Utrecht University (FVMU) was evaluated using a simplified classification tool based on the taxonomy of Bloom. Classifications were made by teaching staff members (subject matter experts, or SME) and by faculty members not involved in teaching the course (non-subject matter experts, or NSME). To compare the cognitive level assigned by raters in the SME group to that assigned by the NSME group, each test item was assigned a modal taxonomic level. The results indicate that the agreement level between a pair of raters within a group (SME or NSME) differed (34% to 77% and linear weighted Cohen's kappa coefficient 0.12 to 0.60). The agreement level on the modal taxonomic level between the SME and NSME groups for the two courses was 65% and 73%, with a linear weighted Cohen's kappa coefficient of 0.43 and 0.63 respectively. The requirement of expertise of a subject for classification is discussed. The introduction of the classification tool had a positive effect on teaching staff members' awareness of the importance of the cognitive level of assessments. Improvements to the classification tool to obtain higher agreement levels are proposed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it