SMALL-GROUP TUTORIALS AND OPEN BRAINSTORMING FOR PROBLEM-SOLVING IN ENVIRONMENTAL ENGINEERING SYSTEMS
Bibliographic record
Abstract
Environmental Engineering Systems is a core-course for undergraduate students at the University of Guelph pursuing degrees in Environmental or Water Resource Engineering. The class is a thorough introduction to many elementary concepts of Chemical and Biological Engineering, including concepts of conservation and reactor system design, and is delivered through lecture, laboratory and tutorial components. At the University of Guelph, Environmental Engineering Systems tutorials are operatedin small groups (approx. 20 students), taking advantage of the extensive whiteboards available in the facility to promote open brainstorming as a means to solve problems. Students work in partnerships, but are encouraged to discuss with the other groups in the room, as to come to a consensus solution on a given problem. Instructors (usually 2) float around the room, coaching students as needed, but refrain from providing over-guidance and a final solution; ensuring students be cognizant of problem solving in industrial and/or higher-learning settings. This technique was beneficial to instructors, allowing for the easy identification of specific problem-solving skills students were lacking, and the appropriate corrections to be implemented. There were still some concerns about engaging timid students, and also with students becoming over-dependent on the group dynamic and not performing as well individually.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".