Local experience of laboratory activities in a BS physical therapy course: integrating sEMG and kinematics technology with active learning across six cohorts
Bibliographic record
Abstract
Introduction Integrating technology and active learning methods into Laboratory activities would be a transformative educational experience to familiarize physical therapy (PT) students with STEM backgrounds and STEM-based new technologies. However, PT students struggle with technology and feel comfortable memorizing under expositive lectures. Thus, we described the difficulties, uncertainties, and advances observed by faculties on students and the perceptions about learning, satisfaction, and grades of students after implementing laboratory activities in a PT undergraduate course, which integrated surface-electromyography (sEMG) and kinematic technology combined with active learning methods. Methods Six cohorts of PT students (n = 482) of a second-year PT course were included. The course had expositive lectures and seven laboratory activities. Students interpreted the evidence and addressed different motor control problems related to daily life movements. The difficulties, uncertainties, and advances observed by faculties on students, as well as the students’ perceptions about learning, satisfaction with the course activities, and grades of students, were described. Results The number of students indicating that the methodology was “always” or “almost always,” promoting creative, analytical, or critical thinking was 70.5% [61.0–88.0%]. Satisfaction with the whole course was 97.0% [93.0–98.0%]. Laboratory grades were linearly associated to course grades with a regression coefficient of 0.53 and 0.43 R-squared (p < 0.001). Conclusion Integrating sEMG and kinematics technology with active learning into laboratory activities enhances students’ engagement and understanding of human movement. This approach holds promises to improve teaching-learning processes, which were observed consistently across the cohorts of students.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".