Improving physiotherapy students' anatomy learning experience and short‐term knowledge retention—An observational study in Malta
Bibliographic record
Abstract
Abstract Anatomy is physiotherapy's foundation. However, undergraduate classroom learning and knowledge acquisition‐retention remain questionable. This study explored the possibility of improving this learning experience and evaluates the gross anatomy of abdomen and pelvis short‐term knowledge retention among first‐year physiotherapy students in Malta. The online Kahoot! game‐based quiz platform was used through an instructor‐designed best‐of‐four multiple‐choice questions. Correctly answered questions and Kahoot! scores generated by the platform were utilized to measure knowledge retention. Kahoot! sessions 1 and 3 shared similar attendance and response rate and were compared together. The Mann–Whitney U test was used to compare Kahoot! scores and Chi test for trend to compare correctly answered questions. Students' perceived learning experiences before and after the introduction of the Kahoot quizzes were gathered through Likert scores and analyzed using McNamar's chi‐square test. Overall, a significantly increased trend in correctly answered questions (χ2: 23.38, p‐value: <0.001) across the Kahoot! sessions were evident. Four questions out of 12 exhibited significant Kahoot! scores differences. Students reported better learning experiences following the initiation of Kahoot! (χ2: 5.1, p‐value: 0.02). Indeed, all students agreed that the use of the interactive quiz improved their anatomy short term knowledge retention. Introducing an online interactive quiz as part of the lecture program may be useful to improve the learning experience and anatomy knowledge retention among physiotherapy students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".