Beyond Kahoot! Reflections and guidelines from a serial gamifying educator on when and how to effectively use games and game elements in anatomical education
Bibliographic record
Abstract
According to Nietzsche, "In every real [adult], a child is hidden that wants to play." In everyday life, playfulness and competition can make routine or dull tasks more engaging and can offer educators opportunities to engage a learner in a more entertaining or interactive manner. Learning anatomy in a meaningful and comprehensive manner requires an ability to memorize an enormous volume of material and then applying the foundational content to make sense of it within real-world applications. Adding game elements or gamification within a teaching and learning space requires considerations by educators on the most effective use of their time, ability, and resources, as well as a clear understanding of the purpose of gamifiying their class or laboratory. Drawing upon 25 years of experience and feedback in implementing gamification and real games for anatomy learners in class, laboratory, and review, this article provides multiple examples and reflections/criteria for when gamification within a curriculum is value-added. The author offers approaches for targeting recall, team building, learner engagement through in-class knowledge checks and review sessions, game assignments, and even critical thinking using bespoke anatomy games addressing real-world application scenarios. Examples include several simple and low-tech options, adaptations of existing games, as well as commercial or custom games, which can be implemented as competitions. The author provides reflections and implementation recommendations for these approaches, the use of reward systems as well as web resources for building, adapting, or utilizing games outright.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".