Incorporating Engineering Skills Training in Engineering Programs
Bibliographic record
Abstract
In recent years, shop training in Canadian public schools has declined. Coupled with the steadily decreasing cost of/increased exposure to new manufacturing techniques, many novice engineering students do not know/appreciate the role of machining in design and manufacturing. This deficit in understanding of manufacturing processes, their abilities and deficiencies, and their role in design is a significant weakness. A short duration activity was created to introduce students to various operations on lathes, mills, and drill presses through the manufacturing of a small keychain. During each session, a pair of students entered the machine shop every 10 minutes and moved through the machining processes with their keychain blanks assembly-line style. Feedback was collected from both student participants, and TA supervisors to inform continuous improvement of the activity. Results indicate that students perception of skill as well as their comfort level on the lathe, mill and drill press all increased. Results also demonstrate that there is an increase in student interest in machining after the completion of the keychain activity. Response from TAs indicated that while supervising the activity was a significant burden on them, overall it was worth it for the student participants.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".