Novel application of gamification to support undergraduate anatomy: Student perceptions and performance
Bibliographic record
Abstract
Anatomy is a foundational component of various medical and paramedical disciplines. Existing research has suggested that games or game elements can improve student interest in musculoskeletal (MSK) anatomy. This project builds on previous gamification and serious game work and incorporates new anatomy-based games into undergraduate anatomy education. Challenging structures and areas of difficulty were identified using previous years' exams. This informed the content and design of the games in this study: a modified Guess Who? and a group-based labeling exercise. Both were provided in optional study sessions available to students in an introductory undergraduate anatomy and physiology course. The data collected includes pre- and post-session questionnaires, labeling times, and midterm exam performance. Few participants reported previously incorporating games into their own MSK anatomy study time, but the majority believed that anatomy-specific games would positively improve their confidence, engagement, and motivation in the course (LIKERT scores all >4.5/5). The games improved students' self-perceived confidence (92% somewhat or strongly agree) and anatomy knowledge (100% somewhat or strongly agree), and provided opportunity for peer collaboration that is often missing in large undergraduate anatomy classes. There were, however, inconsistent effects in exam performance seen across different sections of the course (in-person vs virtual). These findings suggest that the benefits of educational games may depend on contextual factors that require further exploration. Future studies should also explore extending this approach to other challenging topics identified by students.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| 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".