The educational theory basis of team-based learning
Bibliographic record
Abstract
BACKGROUND: Health care providers require the ability to use critical thinking skills and work effectively in a team as a part of an overall set of competencies. Therefore, educational programs should use appropriate methods based in educational theory to effectively graduate learners with these abilities. Team-based learning (TBL) is a method that has been introduced in healthcare education to foster critical thinking skills while students work in high functioning teams. AIMS: This article will show how TBL follows the principles of constructivist learning theory. METHOD: The principles of constructivist learning theory are discussed in relation to the teaching method of team-based learning. The effectiveness of TBL in healthcare education is then reviewed. RESULTS: TBL is learner centered with the teacher acting as an expert facilitator and also provides students with opportunities to expose inconsistencies between their current understandings and new experiences thus stimulating development of new personal mental frameworks built upon previous knowledge. The learning is active using relevant problems and group interaction. Teamwork skills are strengthened by focused reflection on new experiences during the group sessions and on teamwork success by providing feedback to group members. CONCLUSION: Since these aspects are all essential components of constructivist educational theory, TBL is solidly grounded in the theory and is a promising method to strengthen healthcare education.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".