Multiple Perspectives on Engaging Future Engineers
Bibliographic record
Abstract
Background 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. Purpose 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. Scope/Method 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. Conclusions 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.
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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 | 0.046 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.021 | 0.052 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".