Teaching teen titans: An anatomy curriculum using superheroes for middle‐ and high school students in health professions outreach programs
Bibliographic record
Abstract
Health professions outreach programs are important avenues to increase interest in the fields of science, technology, engineering, and mathematics (STEM). One aspect of many of these programs is anatomy, which can be challenging to teach due to its scope. Creative teaching methods, such as the incorporation of examples from pop culture, can help students better access complex scientific concepts. This study aimed to assess the utility of a superhero-based anatomy curriculum as part of summer outreach programs at Rutgers New Jersey Medical School (NJMS). Students completed pre- and post-session surveys about their interest in the fields of STEM, their background knowledge of superheroes, and their thoughts on the effectiveness of using superheroes to learn anatomy. Prior to participating in the curriculum, most students were interested or very interested in the fields of STEM (72.4%). After the curriculum, most students (79.3%) reported that their interest expanded. Almost all students reported that the use of superheroes was beneficial to their learning experience (91.4%) and was preferred over traditional teaching methods (87.9%), despite not necessarily having existing background knowledge or interests in superheroes. Finally, some students felt that seeing the diverse identities of different superhero characters improved their ability to relate to the material. In conclusion, students felt that a superhero-based anatomy curriculum improved their interest in the fields of STEM and their learning experience. This suggests that creative teaching methods can effectively augment the existing mission of health professions outreach programs for a diverse group of 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".