Physical and Virtual Environment for Automation Education of Engineers and Technicians, Part 3: Improvement of joint distant laboratory activities in process control and automation based on past experience
Bibliographic record
Abstract
Current industrial software technology offers the possibility for the plant staff to operate production systems without an obligate need to be present on the production floor. Our project, which earlier phases were presented in the last CEEA conference, proposed joint learning activities related to this reality for the training of future engineers and technicians. It is a collaboration between the Université du Québec à Rimouski (UQAR) and the Cégep de Rivière-du-Loup (CEGEP), both located in the eastern Quebec region. Activities were conducted under two different themes: (i) process control activities using a hydraulic training setup, and (ii) sequential automation activities using individual units of a mini-plant designed for recycling beverage bottles and cans. The third phase of the project, being the subject of this paper, was to improve the virtual environment and laboratory activities from past experience results, and evaluate the effectiveness of these modifications. To state a few modifications as examples, the activities contexts were modified from a customer-client relation between the teams to a more cooperative scenario, as distant and local implementation teams from the same service provider. The sequential automation activity was conducted on a more complete and operational mini-plant, and the student teams were involved in a more interactive work, involving exchange of files and sharing common work objectives. All activities started with a physical encounter between the student teams, which effectively helped to improve comradeship. Results once again showed that students from both institutions successfully worked and communicated together despite their different skills and backgrounds. Training objectives for this phase were successfully attained and the lessons learned were exploited to effectively enhance the training level and the studentacquired skills through the activities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".