The use of limericks to engage student interest and promote active learning in an undergraduate course in functional anatomy
Bibliographic record
Abstract
The study of anatomy is a content-dense discipline with a challenging vocabulary. A mnemonic is a series of letters, a word, a phrase, or a rhyme that students can use when studying to facilitate recall. This project was designed to promote active learning in undergraduate students studying anatomy and physiology by asking them to create limericks based on course content and then to evaluate the limericks written by their peers for learning value, accuracy, style, and adherence to limerick characteristics. Students (278 and 288, respectively, in the 2009 and 2010 sections of ANP1107) worked in groups of three to create a total of 242 limericks. Peer evaluation was accomplished in two stages using a 20-point marking rubric. In Stage 1, students were randomly divided into 10 groups (n = 23 ± 2 students) with each group member evaluating the same 12 ± 1 limericks. In Stage 2, the top 19% of limericks were reevaluated by all students so that the best three could be chosen. In each of the two years, 60% of students completed all parts of the assignment. Higher percentages (75-80%) participated in limerick writing and one of the two assessment stages. A positive association was noted between level of student participation in the limerick assignment and final course marks. Limerick creation and evaluation can be used to promote active learning by encouraging students to review functional-anatomy-based content to create limericks with good learning value and to provide valid assessments of limericks written by their peers.
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 | 0.005 | 0.017 |
| 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 | 0.000 | 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".