Drawing “Octo-Pines”: Ice-Breaker Active-Learning Activities to Introduce Drawing-to-Learn in Biology
Bibliographic record
Abstract
Drawing has been advocated as a technique to develop visual literacy and observational skills in biology students. To increase student motivation and confidence to draw in our course, we developed an introductory active-learning lesson with a series of icebreaker activities that promote student creativity and discussion. These activities include a clicker question, group discussions, drawing activities, and a worksheet. During the lesson, student responses generated more than 18 categories of how visuals can be used as a professional practice and as a learning tool in biology, with 14 of these categories overlapping. Students demonstrated interest in using a variety of drawings and visuals to represent various scientific scenarios. In a survey completed after the lesson, students reported that this activity increased their understanding of how drawings are used in the profession of biology and as a learning technique. Students also indicated that while they experienced some discomfort with the exercises, they enjoyed the drawing activities and found them useful. The examples in this lesson can be adapted to fit courses that emphasize drawing, observation, or visual literacy. Primary Image: “Octo-pine.” To increase student motivation to draw in zoology, the last activity in this lesson asks to students to draw an invertebrate-food combination (e.g., “octopine” = octopus + pineapple; “BEErito” = bee + burrito).
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.031 | 0.005 |
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".