HOW STUDENTS PERCEIVE THE MANY ROLES THEY MUST PLAY IN AN ENGINEERING LABORATORY COURSE
Bibliographic record
Abstract
This work focuses on the unit operations laboratory course given by the Chemical and Biotechnological Engineering Department at Université de Sherbrooke. In order to help students develop their organisation skills, our department developed a formula based on "directive teams" and "operative teams" in which each team is put in charge of one of 9 experimental setups for the duration of the semester. A team is said to be "directive" when they are working on their assigned unit operation and "operative" when they are working on the other unit operations. As part of a directive team, the students must elaborate an experimental plan and protocol that they will ask the other teams to carry out. The success of this formula depends both on the ability of the teaching team as well as on the student-student work environment. The present project seeks to better understand the work environment in which the students carry out this task by polling them about their experience in the unit operations laboratory course given by our department.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".