Hijacking All The Courses: A Transdisciplinary Learning Experience for Undergraduate Students
Bibliographic record
Abstract
Shoshanah R. Jacobs, Jessica Nelson, Brianna Driscoll, Daniel Gillis University of Guelph\nWe present Ideas Congress (ICON), a truly trans-disciplinary learning opportunity for undergraduate students at the University of Guelph. A review of interdisciplinary or trans-disciplinary research opportunities for undergraduate students in Canada revealed that programs were either 1) restricted to graduate programs or 2) not very trans-disciplinary.\nUsing a consultative process with faculty and administration, we designed a pilot course that focuses on the teaching of knowledge translation and transfer theory and then challenges students to apply that knowledge to solving a community problem.\nICON has three major goals:\nTo facilitate transdisciplinary learning and research.\nTo strengthen discipline specific knowledge by providing students with the appropriate platform and tools to act as teacher.\nTo engage students in community supported research and challenge them to make their work relevant and accessible to all stake holders.\nIn the first offering of the course, we had 25 students from 14 different majors and 4 Colleges. Thirteen different advisors from those 4 Colleges accepted these students into the respective courses, two professors from different Colleges taught the course, one professor and one PhD candidate from different Colleges conducted research on the outcomes of the course and three undergraduate students from two Colleges provided research assistance. ICON was made possible by accessing all single semester senior undergraduate independent study courses across campus, allowing students to make a choice: work one-on-one with a faculty advisor in the traditional way, or join ICON and gain experience in working in research with a trans-disciplinary team after having learned how.\nHere we demonstrate that a truly trans-disciplinary learning experience is possible within the traditional academic framework with only minor adjustments. We show that the creation of a new course code (therefore removing the possibility of getting credit towards respective majors) is not necessary and that this is likely to increase the demand for this experience.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".