Promoting Interdisciplinary Learning: A Cross-Course Assignment for Undergraduate Students in Advanced Biology and Drawing Courses
Bibliographic record
Abstract
Exposing students to interdisciplinary learning experiences has many benefits including a richer understanding of complex problems, promoting metacognition, and recognizing how to effectively use their skills. For biology students, immersion in creativity and design-thinking inherent to the arts can aid in their ability to imagine, innovate and communicate science. For art students who have been trained to develop unique visual vocabularies, an important learning experience would be to communicate didactic knowledge of an unfamiliar domain in a way that is visually compelling, imaginatively open-ended and informative. For both cohorts of students, academic training at the undergraduate level has remained somewhat sequestered in disciplinary silos. If we are to truly prepare students for careers in which they innovate, work collaboratively, solve problems and continue to learn, then we must provide authentic inquiry-based assignments that allow them to see the value of such proficiencies. This was the rationale behind designing this interdisciplinary assignment for biology and art students at the University of Toronto Scarborough. Here, we report on the design, implementation and insights from the pilot offering of this assignment. We hope that this provides a useful teaching tool for instructors interested in promoting interdisciplinary teaching and learning at the undergraduate level.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".