Design as a second language. Design as a multicultural-multidisciplinary space of integration: Challenges and advantages of introducing design to non-design students, in a second language, in a new cultural context
Bibliographic record
Abstract
Teaching design to design-illiterate students is usually a common case for every first year class instructor at any design program. In addition to this, a particular combination of extra challenges makes Design Fundamentals at -the University of Alberta- a very special spot to learn and teach design. Most sections of this class are open to students from many other fields and levels, from psychology to engineering, and from first year students to senior students. Masters students, who usually come from various countries, are often appointed as teaching assistants as part of the graduate program experience. Some of them choose to stay and teach upon graduation. Diversity is even more distinct amongst undergraduate student. In 2010-2011 this university received about 5800 international students from more than 140 countries, three times larger than the figures of 2001, and increasing every year. The combination of multidisciplinary and cultural diversity from both sides, teachers and students, is a symbiotic and synergetic phenomenon that offers additional challenges and opportunities. This paper intends to describe the experience of teaching-learning design under this environment and ultimately depict the Design Fundamentals classes as a space of integration.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".