Movement Guided Learning as an Efficacious, Effective, and Evidence‐based Teaching Strategy Within the Undergraduate Anatomy Classroom
Bibliographic record
Abstract
Within the anatomy classroom, physical movements and stretches can turn a student's own body into their personal educational tool. Such direct relevance can be useful for the undergraduate junior learner as they attempt to truly understand musculoskelatal anatomy in a meaningful way. This presentation describes the longitudinal, iterative scholarship that went in to developing, piloting, refining, and experimentally assessing Movement Guided Learning (MGL)©, a self‐directed learning workbook that guides students through applications of musculoskeletal anatomy including physical movements/stretches, surface palpations/visualizations, and extensions of knowledge with case‐based scenarios. Most recently, components of the refined MGL workbook were fully integrated as adjunct teaching activities into a large undergraduate classroom within health sciences. Using a randomized control design, the entire curriculum of muscular anatomy was divided into three equal groups; students received MGL activities for 1/3 rd of the taught muscles, students received multiple choice style review questions for 1/3 rd of the taught muscles (as an active, time‐matched control), and students received no additional learning material for 1/3 rd of taught muscles (as a non‐active control). To facilitate full integration of supplementary teaching materials, the course instructor was been made aware of group allocations, provided with all teaching materials, and embedded those materials during in‐class lecture time and during independent student activities. Final exam scores will be segmented and compared in agreement with the three experimental groups, allowing for an intra‐individual superiority assessment of MGL. Student survey responses will allow for MGL efficacy to be further assessed against student learning preferences as well as self‐reported strategy utilization. To control for inadvertent instructor‐bias, time spent on each muscle will be quantified (using video lecture capture software) and compared across the three groups There are currently 187 undergraduate students enrolled in the course with the majority of them enrolled in a global health degree (~75%) in their first year of undergraduate studies (~90%). MGL efficacy will be determined using the aforementioned mixed‐methods assessment strategies following the fall semester final exam. Based on previously reported MGL success, it is hypothesized that the MGL activities will be a useful learning adjunct, especially for students who display kinesthetic learning preferences. Support or Funding Information none to declare
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".