Bibliographic record
Abstract
This paper compares and contrasts some of the most popular taxonomies used in education, including: original Bloom’s taxonomy, revised Bloom’s taxonomy, Webb’s depth of knowledge, SOLO taxonomy, Fink’s taxonomy of significant learning, Shulman’s table of learning, and Marzano’s taxonomy. After a brief outline of each taxonomy, the paper discusses the literature corresponding to their use in education and the taxonomies are compared with regard to their treatment of knowledge, cognition, metacognition, higher-order thinking skills, affect, and explicit or implied theories of learning underlying each taxonomy. This is followed by a discussion of future directions for taxonomies in education. To date, while a few binary comparisons of taxonomies have been published, there has been no broad comparison of what may be regarded as the major taxonomies in use in education today. This paper represents the first broad examination of taxonomies that have had significant impacts on higher education.
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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 | 0.052 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.042 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".