Global Design Studio: advancing cross-disciplinary experiential education during the COVID-19 pandemic
Bibliographic record
Abstract
The impact of COVID in Higher Education has seen universities worldwide shifting to remote and online formats of teaching delivery. In design education, this shift has impacted Experiential Education (EE) pedagogical approach to studio teaching, an approach that gives students an opportunity to apply theory to a concrete experience in a reflective manner and provides cross-disciplinary learning opportunities. This paper discusses Global Design Studio (GDS), a collaborative cross-disciplinary teaching initiative between three design disciplines across three continents: Industrial Design in Australia, Interaction Design in Canada, and User Experience Design in Germany. The objective was to develop a support framework during emergency situations to allow facilitating cross-disciplinary EE to design students. We discuss the three teaching experiences as case studies that offer opportunity for deep analysis and reflection of challenges and enablers to EE education in the shift from traditional design studio to remote and online delivery. While navigating COVID-19 barriers to EE education, GDS aimed to achieve these objectives by sharing resources, ideas and expertise accross the three universities. Each unit dedicated the entire semester program to our first exploration through GDS through a semester project ‘Interactive Mannikin for children to learn CPR techniques’. In this article we discuss the context and outcomes of EE teaching and learning experiences at each unit, as well as lessons we learned as design educators about: inter disciplinarity, inter-intra-cultural issues, group working, timing, remote collaboration, and proposal for a GDS model for cross-disciplinary EE.
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 | 0.023 | 0.015 |
| 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 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".