The rise of design in higher education and non-design students’ experiences of learning through design
Bibliographic record
Abstract
An interest in design integration across school subjects emerged in the 1990s because of pressures on teachers to develop competencies deemed valuable to students for the 21st century, such as problem-solving skills and creative thinking. The 2000s also witnessed a rise in design across higher education curricula. Like schoolteachers at the turn of the century, educators in universities started looking for ways to catch up with their institutions’ move toward marketization initiatives. This article compares the rise of design in schools with higher education. It shows that in both cases, design was adopted as a response to calls for teaching students economic-driven educational goals. The article also provides a possible explanation of why, unlike the variation in types of design taught to school students, design-based learning in higher education focuses on one type, and that is design thinking. The article then presents findings from a qualitative interview study with non-design students across programs who challenge us to consider other types of design they experience and how design learning outcomes are valuable to higher education students beyond market-centric goals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".