Enhanced remote laboratory work for engineering training
Bibliographic record
Abstract
Today, with the development of digital equipment and the increased performances of Information and communication technology (ICT), the online laboratory or Lab At Distance (LAD) found an interesting tool in training systems and can also provide enrichment for the conventional laboratory. In this paper, we present the experience of developing a set of LAD performed by the school of engineering École de technologie supérieure (ÉTS) in Montreal and three colleges (CEGEP) in Quebec. From this experience, the article highlights both technical and pedagogical strengths. The different choices adopted for LAD are discussed and a preliminary assessment of the operation of this set of LAD is also presented. In addition to the beneficial sharing of facilities between institutions, new opportunities of ICT enrich the laboratory work and give it a new dimension. Visiting a Web site for an industrial application related to laboratory work allows contextualization of this work and gives more meaning to the work requested. The exploration of the technological characteristics of the equipment used can provide additional valuable learning. These new technological possibilities coincide with the emergence of new learning approaches and raise questions about the potential role of laboratory work in the training of engineering students. The techno-pedagogy is currently revolutionizing the way traditional training by bringing the experience of the laboratory in the classroom, at home and in various locations. This also allows the student to be more in touch with the technological reality of the laboratory and even industrial space through virtual tours.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".