The Future of Engineering Education: Part 1. A Vision for a New Century
Bibliographic record
Abstract
When we walk into an arbitrarily chosen engineering classroom in 2000, what do we see? Toooften the same thing we would have seen in 1970, or 1940. The professor stands at the front of the room,copying a derivation from his notes onto the board and repeating aloud what he writes. The students sitpassively, copying from the board, reading, working on homework from another class, or daydreaming. Once in a while the professor asks a question: the student in the front row who feels compelled to answeralmost every question may respond, and the others simply avoid eye contact with the professor until theawkward moment passes. At the end of the class students are assigned several problems that require themto do something similar to what the professor just did or simply to solve the derived formula for somevariable from given values of other variables. The next class is the same, and so is the next one, and the oneafter that.There are some differences from 30 years ago, of course. The homework assignments require theuse of calculators instead of slide rules, or possibly computers used as large calculators. The math is moresophisticated and graphical solution methods are not as likely to come up. The board is green or white ormaybe an overhead projector is used. Nevertheless, little evidence of anything that has appeared in articlesand conferences on engineering education in the past half-century can be found in most of our classroomsand textbooks. In recent years, however, there have been signs of change.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".