Different student perceptions of the effectiveness of Team Based Learning exercises between undergraduate health sciences and medical students
Bibliographic record
Abstract
In the fall of 2013 we substituted a didactic lecture for a Team Based Learning exercise (TBL) in an undergraduate Anatomy and Physiology course with an enrollment of 285. We also substituted a didactic lecture for an on‐line video, and ran a TBL exercise during the previously scheduled lecture slot for the neuroanatomy block of an MD class with an enrollment of 160. The exercise consisted of 1) an individual readiness assessment (five MCQs) test (iRAT), 2) review of the iRAT questions, 3) separation of the students into teams to discuss a case study, and 4) a team effort in answering a multiple choice question based on the case study (gRAT). The iRAT and gRAT scores were recorded using an iClicker (undergraduate) or Scantron card (MD). The results were similar for the undergraduate class over two years. 80% of undergraduate students felt that the iRAT helped keep them on task, 54% felt that the iRAT prepared them for the MCQ Midterm exam, and 42% felt that the TBL allowed them to discuss concepts that they would not otherwise have considered. However, more than 53% of students ranked the TBL as the least liked activity in either this course or any other course they have taken. The paradoxical response was attributed by undergraduate students to the stress and the time required to prepare for the exercise (54%). In contrast, the MD students ranked the TBL exercise (67%) higher than the iRAT (63%) and just as valuable as dissection (67.3%) for their learning. We conclude that factors determining how students rank the TBL learning activity include the size and makeup of the TBL groups, whether the activity is part of a summative evaluation, and the experience of the student learner.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".