Bloom’s dichotomous key: a new tool for evaluating the cognitive difficulty of assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ONE OF THE MORE WIDELY USED TOOLS to both inform course design and measure expert-like skills is Bloom’s taxonomy of educational objectives for the cognitive domain (2, 13, 22). This tool divides assessment of cognitive skills into six different levels: knowledge/remember, comprehension/understand, application/apply, analysis/analyze, synthesis/create, and evaluation/evaluate (2, 6). The first two levels are generally considered to represent lower levels of mastery (lower-order cognitive skills) and the last three represent higher-order levels of mastery involving critical thinking (higher-order cognitive skills) with apply-level questions often bridging the gap between the two (e.g., Refs. 5, 8, 10, 11, 23, and 24). While Bloom’s taxonomy is widely used by science educators, learning and mastering the concepts of the cognitive domain to categorize educational materials into the six levels identified in Bloom’s taxonomy are not trivial tasks. As with any complex task, experts and novices differ in the key abilities needed to cue into and evaluate information (4, 7, 9). Across disciplines, novices are less adept at noticing salient features and meaningful patterns, recognizing the context of applicability of concepts, and using organized conceptual knowledge rather than superficial cues to guide their decisions. Newer users of Bloom’s taxonomy demonstrate similar difficulties as they work to gain expertise, leading to inconsistencies in Bloom’s ratings (1, 8, 15) (see BDK Development for examples).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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