Multiple Perspectives on Engaging Future Engineers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
B ackground Engaging future engineers is a central topic in everyday conversations on engineering education. Considerable investments have been made to make engineering more engaging, recruit and retain aspiring engineers, and to design an education to prepare future engineers. However, the impact of these efforts has been less than intended. It is imperative that the community reflects on progress and sets a more effective path for the future. P urpose The purpose of this article is to map a new innovation landscape for what it means to engage future engineers. This is a theoretically grounded divergent‐thinking effort to enable a broader space of high impact innovations for engaging future engineers. S cope /M ethod A multiple perspectives methodology drawing from innovation, cross‐disciplinary, and boundary work frameworks was used to make visible multiple facets of engaging future engineers. Scholars from diverse communities of thought and discourse were selected to present interparadigmatic perspectives, act as boundary agents, challenge and transform current ways of thinking, and illustrate new opportunities for engineering education innovation. C onclusions A new innovation landscape for engaging future engineers is needed, one that emphasizes epistemological development and social justice, new configurations on engineering thinking and connecting to the formative years of development, the entwinement of engineering knowing and being, and mutually informing consequences for opening up a broader space for innovation. We also need to adopt strategies and tools for using a multiple perspectives approach to better understand complex engineering education problems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it