DNA barcoding in diverse educational settings: five case studies
Bibliographic record
Abstract
Despite 250 years of modern taxonomy, there remains a large biodiversity knowledge gap. Most species remain unknown to science. DNA barcoding can help address this gap and has been used in a variety of educational contexts to incorporate original research into school curricula and informal education programmes. A growing body of evidence suggests that actively conducting research increases student engagement and retention in science. We describe case studies in five different educational settings in Canada and the USA: a programme for primary and secondary school students (ages 5-18), a year-long professional development programme for secondary school teachers, projects embedding this research into courses in a post-secondary 2-year institution and a degree-granting university, and a citizen science project. We argue that these projects are successful because the scientific content is authentic and compelling, DNA barcoding is conceptually and technically straightforward, the workflow is adaptable to a variety of situations, and online tools exist that allow participants to contribute high-quality data to the international research effort. Evidence of success includes the broad adoption of these programmes and assessment results demonstrating that participants are gaining both knowledge and confidence. There are exciting opportunities for coordination among educational projects in the future.This article is part of the themed issue 'From DNA barcodes to biomes'.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".