Movement guided learning© as a novel means of musculoskeletal anatomy instruction
Bibliographic record
Abstract
Our bodies can be physically stretched, palpated, and manipulated. These movement opportunities can be used in anatomical instruction, essentially turning a student's body into their own educational tool. This research details the initial development and use of a novel teaching approach, Movement Guided Learning© (MGL). Following prior instruction on both the skeletal system and the muscular system, students participated in a tutorial‐styled learning activity that relied on the MGL teaching approach. The learning activity was exploratory, guiding students through physical movements/stretches, directing students through surface palpation/visualization, and challenging students to collectively brainstorm on the application concepts. Musculoskeletal anatomy knowledge was assessed before and after the MGL activity, with improvements on open‐ended concept questions (t(9) = −3.65, p=.005). The MGL activity was well received by students, with all students indicating they believe it should be used in future classes. MGL as a means of musculoskeletal anatomy instruction provides an opportunity for students to independently construct deeper, more integrated, concept understanding. Grant Funding Source : none
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".