Yoga asanas as an effective form of experiential learning when teaching musculoskeletal anatomy of the lower limb
Bibliographic record
Abstract
Physical movement as a conduit for experiential learning within the academic context of anatomy is a strategy currently used in university dance education. This same approach can be applied to other movement-based practices, for example, yoga. The primary purpose of this study was to pilot a novel teaching curriculum to yoga practitioners, based on Bruner's Theory of Instruction, which incorporated the four adaptive modes of Kolb's Theory of Experiential Learning. The secondary purpose was to assess the applicability of anatomical knowledge within the participants' yoga practice. Following the development of a curriculum appropriate for a spectrum of academic backgrounds, participants were recruited to attend a 2-hour learning session within the Department of Anatomy at Queen's University in Kingston, Ontario, Canada. The learning session guided participants through the bones and muscles of the lower limb pertaining to five specific yoga poses. Based on participant feedback, the sessions were positively received and consistent. In addition, learning session participants were able to apply the anatomical information they were taught to their yoga practice 1-month later. Bruner and Kolb's independent theories on curriculum design and effective learning practice were successfully incorporated to create a 2-hour learning session. The potential use of experiential learning to compliment and/or enhance traditional didactic teaching in the academic context of anatomy should be further explored.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".