Preparing Students for Practical Exams: The Dreaded Anatomy Bell Ringer
Bibliographic record
Abstract
Undergraduate students in the health sciences typically perform poorly on practical exams. For example, the bell ringer is a stressful part of anatomy courses. This poor performance may be due to the fact that students often struggle with ‘transferring’ content from lecture (i.e., classroom) to a clinical setting (i.e., lab; Bolander et al., 2008). Although students are provided with weekly lab periods to interact with anatomical models, this learning is typically quite passive. As no assessments occur before the bell ringer, students have no early opportunities to test their knowledge. Course teaching assistants (TAs) are uniquely positioned to prepare students for the exam and to ensure they are meeting required learning outcomes. Workshop participants will explore facilitation strategies to actively involve students in the lab, thus aiding students to deepen their understanding of human anatomy and motivating reflection on these lessons. Moreover, this workshop will highlight the importance and utility of practical exams in anatomy courses and will also help increase participants’ confidence in helping students apply and evaluate their lecture-based knowledge in lab.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".