Introducing DNA concepts to Swiss high school students based on a Brazilian educational game
Bibliographic record
Abstract
Subjects such as techniques for genetic diagnosis, cloning, sequencing, and gene therapy are now part of our lives and raise important questions about ethics, future medical diagnosis, and such. Students from different countries observe this explosion of biotechnological applications regardless of their social, academic, or cultural backgrounds, although they are not usually familiar with their theoretical genetic bases. To introduce some molecular biology concepts for high school students, we developed a new problem for the Brazilian board game "Discovering the cell" ("Célula Adentro©" in Portuguese), a pedagogic tool based on inquiry-, cooperative-, and problem-based learning. This problem (Case) is based on the forensic DNA, which represents an interesting theme for students, as it recurrently appears on newspapers and television series. In this work, we tested this game with secondary students and teachers from Switzerland. Our results indicate that the game "Discovering the cell" is well accepted by both students and teachers and may represent a good pedagogical approach to help teaching complex themes in molecular biology, even with students from different socioeconomical, cultural, and academic backgrounds.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".