Hands-on Training and International Experience in Engineering Education
Bibliographic record
Abstract
The holistic training of an engineer includes hands-on experience and theoretical study. Engineering at the University of Waterloo (UW) is unique because of its co-op program, which exposes students to practice of engineering in industry, interwoven with academic terms in the university.This paper details conduct of International Industrial Training Programs for the students of UW. These programs conducted over the past five years have addressed both issues of hands-on training and internationalization of engineering education. These programs provide skills and training on use of tools and machines culminating in a design and build project experience. This paper emphasizes safety and security in planning and implementing the programs in a foreign country. It also discusses issues related to international travel and cultural challenges faced by students and organisers. It summarizes the gains for the engineering students from the program based on immersive international exposure to a foreign culture through personal interactions with locals and planned trips, industrial practices, study of preparatory engineering courses like machining, welding, foundry, wood working, properties of materials and computer aided design leading to student designed and built projects.Five decades ago, when the university was founded, the students came from a demographic, that was split between a majority from rural and small urban centers. The students had some experience with tools and machines at high school and at home, as cars were simpler and home repair was a common activity. This was especially so on farms and in rural environments. This has gradually shifted to the present day when students primarily come from large urban centers and are very competent in working with high-tech gadgetry like computers, communication devices and entertainment consoles. Conversely, nowadays most high school children are not exposed to working safely with tools and machines.Over time co-op jobs have also shifted from hands-on engineering and shop floor work, to softer jobs using computers for design, analysis and managerial tasks. This has increased the challenge of engineering education where the facilities for teaching hands-on skills in the university are deterred by the lack of time in the curriculum, shortage of equipment and trained instructors. The economic environment of our nation has also evolved from a national manufacturing based economy a few decades ago, to a global economy of today. Industries have gone global in their routine functions, requiring engineers to routinely work in collaborative work with functionaries of their industry in other countries, with different culture, language and skill sets. It has thus become vital for engineering education to adapt and introduce the students to aspects of global engineering environment in the curriculum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".