Physiotherapy Students’ Perceptions of Team-Based Learning Using the Team-Based Learning Student Assessment
Bibliographic record
Abstract
According to the National Curricular Guidelines for the undergraduate-level course in physical therapy, the curriculum must promote the training of a generalist, humanist, critical, and reflective professional to develop skills focused on decision-making, communication, leadership, etc. Several methodologies are used in the teaching-learning process; of these, active methodologies are often cited, wherein, contrary to the traditional model of teaching, the roles of the teacher and student are reversed. This study aimed to evaluate students’ perception of learning in teams (Team-based learning) as a teaching-learning strategy in the physical therapy undergraduate program of the University of the State of Pará, using the Team-based Learning Student Assessment Instrument (TBL-SAI). A cross-sectional, descriptive study with 21 physical therapy undergraduate students of the UEPA Campus XII was conducted. The TBL-SAI was administered after they participated in an optional course of the respiratory system, wherein the TBL teaching was adopted. The 33 items of the TBL-SAI comprise responses ranging from 1 (I strongly disagree) to 5 (I strongly agree), divided into three subscales: perception of students’ accountability, preference for traditional approach or TBL, and students’ satisfaction. Average score on the subscales higher than the neutral scores indicated that the students perceived the TBL to be an effective learning tool. The participants reported an overall positive experience using TBL with respect to accountability toward their studies, preference for TBL, and their satisfaction with the method. Future studies should evaluate the impact of TBL on the academic performance and learning ability of undergraduate students of physical therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".