Taxonomies in Education: Overview, Comparison, and Future Directions
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
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.
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 | 0.000 | 0.000 |
| 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