Game-Based Learning Outcomes Among Physiotherapy Students: Comparative Study
Bibliographic record
Abstract
BACKGROUND: University teaching methods are changing, and in response to a classical teacher-centered approach, new methods continue to strengthen knowledge acquisition by involving students more actively in their learning, thus achieving greater motivation and commitment. OBJECTIVE: This study aimed to analyze the degree of satisfaction of physiotherapy students who used a board game-based approach, as well as to compare the difference between traditional and gamification teaching methods and their influence on the final evaluation of these students. METHODS: A comparative study was conducted. Participants were physiotherapy students who were enrolled in the subject of "physiotherapy in geriatric and adult psychomotricity" (n=59). They were divided into two groups (experimental [n=29] and control [n=30] groups) through convenience sampling. The experimental group received gamification lessons, where the students performed different tests adapted from Party&Co, and the control group received traditional lessons. A total of 16 theoretical lessons were received in both groups. RESULTS: The scores in the final examination of the subject were higher in the experimental group (mean 7.53, SD 0.95) than in the control group (mean 6.24, SD 1.34), showing a statistically significant difference between the two groups (P=.001). CONCLUSIONS: Overall, the "Physiotherapy Party" game not only stimulated learning and motivated students, but also improved learning outcomes among participants, and the improvements were greater than those among students who received traditional teaching.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".