Transcending Lockdown: Fostering Student Imagination through Computer-Supported Collaborative Learning and Creativity in Engineering Design Courses
Bibliographic record
Abstract
Engineering design and communication courses are typically dynamic, active learning spaces that bring together a complex array of knowledge and skills. Their ambiguous nature has allowed, often contentiously, subjects such as language and communication, the arts, the humanities, and the social sciences to enter the discourse of engineering in a newly meaningful way. This article considers this development in the context of the COVID-19 pandemic and, in particular, how the creativity and imagination required to succeed in engineering design might be cultivated in emergency distance learning. I consider a plethora of sources for guidance, with a special interest in how language and communication facilitates collaborative learning, creativity, and intersubjectivity and how that mediation is further mediated by educational technology in distance learning. I focus on the challenges faced and the resulting importance of training for both instructors and students. Finally, I argue that, despite our difficult circumstances, we should aim to encourage our students to exercise their imaginations, both independently and collaboratively, through our selection, framing, and facilitation of team design projects during the pandemic.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".