Developing a Classification Tool Based on Bloom’s Taxonomy to Assess the Cognitive Level of Short Essay Questions
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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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 | 0.040 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".