"Are You Stronger Than a Lemur?" An effective, interactive <scp>STEM</scp> outreach program for increasing anatomical and biomechanical knowledge across diverse populations
Bibliographic record
Abstract
Access to high-quality outreach programs is crucial for preparing students for STEM careers, yet traditional classrooms often lack diverse, hands-on learning opportunities, particularly in anatomy and evolutionary biology. We present "Are You Stronger Than a Lemur?"-an interactive STEM activity that introduces K-12 students to fundamental concepts in anatomy, evolution, physics, and data analysis through real-world applications. Participants formulate hypotheses, collect and analyze data, and engage with age-tailored educational materials that support differentiated learning. We assessed the program's effectiveness through pre- and post-program knowledge assessments across 1670 participants (1045 eligible responses) from the United States and Mongolia. Results showed a significant increase in knowledge acquisition in anatomy, evolution, physics, statistics, and zoology. After controlling for confounding variables, we also observed a significant increase in interest in STEM careers. "Are You Stronger Than a Lemur?" bridges gaps in STEM education, particularly in underrepresented fields like anatomy and evolutionary biology, by providing an adaptable program suited to different age groups, genders, and countries. Its success lies in connecting theoretical concepts to tangible data, fostering critical thinking, problem-solving, and data interpretation skills. The program not only reinforces core STEM concepts but also offers students a unique, engaging experience that deepens their understanding and enhances their potential for future STEM careers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".