Using Practical‐Based Team Based Learning as a Tool For Providing An Immediate Feedback to the Students During Learning Anatomy
Bibliographic record
Abstract
Background Although assessment of learning may promote deep learning, it does not provide immediate feedback to drive further learning and training. In addition, with the students being subjected to formative exams during the course of their study, feedback might not be provided appropriately and timely. Students would like to understand and use the reasoning behind judgments and they demand that practical assessment criteria be explained. Team‐based learning (TBL) is a student‐centered learning strategy, which has been confirmed in medical education to enhance learning in small groups. Nevertheless, it has not been implemented during practical anatomy learning that challenges the spatial perception of the learned material in contrast to other disciplines. This study aims to present a novel intervention in using practical‐based TBL in anatomy and its impact as a tool for providing immediate feedback. It also determines students' perceptions of the practical‐based TBL and the effect of the given feedback on anatomy learning. Method An objective structured practical examination (OSPE) formative test setup was used. Students took the test in two successive formats: individually (iRAT) and in teams (tRAT). Individual students rotated around the practical stations in the form of a steeplechase examination during the iRAT. For the subsequent tRAT, photographs of the stations were projected in the classroom to groups of eight students each. The session was concluded by discussing the answers with the tutor who provided an immediate feedback. Students' perception (N=110) was measured using quantitative and qualitative instruments through a self‐administered questionnaire and a focus group discussion. Results The students perceived that the practical‐based setup was a useful tool in providing immediate feedback. They also agreed upon the fact that apart from highlighting areas of their weakness (86%); the practical‐based TBL also provided diverse options for testing knowledge (79%) and further stimulated motivation in them to attend these sessions (80%). Seventy eight of the students indicated that it boosted their self‐confidence to face the examinations, and 80% were able to clarify the reasoning behind judgments. Conclusion Practical‐based TBL is a valuable learning strategy and can be employed as an effective tool for providing immediate feedback during anatomy learning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".