Active Learning: Using Bloom's Taxonomy to Support Critical Pedagogy
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
Many education systems use a primarily passive approach to learning. So that students may have a deeper and more meaningful learning experience, educators can use an active learning approach. This approach attempts to engage students at higher levels of thinking so that they are more interested in, better engaged with, and understand better the course material. Critical pedagogy, on the other hand, focuses on empowering students to become agents of social change for greater equity and justice. Although critical pedagogy is often seen as giving education a political goal, it is actually a good concrete example of applying active learning principles in a classroom. To better understand the relationship between active learning and critical pedagogy, this paper will explore how Bloom's taxonomy can describe the activities involved in active learning and how those activities are necessary for critical pedagogy.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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