Digital design and student learning through videoconference collaboration
Bibliographic record
Abstract
This paper reports on a pilot study involving a long distance learning experiment between the University of Adelaide and Penn State University through a six-week videoconference program. The program involved staff and students from digital media courses within each University, including Dr Dean Bruton, Senior Lecturer in the School of Architecture, Landscape Architecture and Urban Design at The University of Adelaide, and Associate Professor Madis Pihlak, Director of The Stuckeman Center for Design Computing, School of Architecture and Landscape Architecture, Penn State University. Using Information and Communications Technologies (ICT) for teaching digital design processes has many advantages and disadvantages. Instant communication between groups and individuals across the world, defies the barrier of distance. Interdisciplinary exploration and collaborative action allow the expansion of design curriculum possibilities and the sharing of information and experience, while technical skills and standards rise as students find new levels of potential in response to more diverse audiences. Disadvantages with such design experiments include time differences between two continents, technical constraints and the availability of technical assistance. The project was largely successful, evident through positive feedback from staff and students, and the emergent relationship between the two schools. Through this pilot study, and the resulting research, new possibilities are now being explored, including cross- continental design collaboration with design schools in Canada, Malaysia and India. The University of Adelaide, has supported the project by supplying a AUS$48,000 grant to purchase the test equipment, used in the pilot study, and establish a dedicated videoconference facility.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".