The dandelion evolution outreach program: learning through inquiry-based community engagement
Bibliographic record
Abstract
Science outreach programs have positive effects on students in both elementary and high school, but are often developed as internships, thus limiting access and requiring significant financial investment. Several larger scale evolution-themed outreach programs have been developed in the United States where academic institutions are addressing a specific need to actively promote science-thinking in direct opposition to other ways. This context is unfamiliar in other countries. Here we present a pilot implementation of the Dandelion Evolution Outreach Program designed to provide an inquiry-based learning opportunity in evolution for grade 11 high school students in Ontario, Canada. This program is flexible with respect to time commitment, low cost, is applicable throughout North America and many other regions of the world, and is learner-centered through active learning with both simulation and inquiry-based activities. We found that students were engaged with our lesson plan including both the simulation and inquiry-based activities. Results of our post-assessment suggested that students were able to formulate appropriate predictions relevant to the concepts of natural selection. The scalability of this program will be demonstrated further as more schools become involved in future offerings. The Dandelion Evolution Outreach Program is an effective means of engaging secondary school students in active, inquiry-based learning that does not restrict access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".