AN OPEN-ENDED DESIGN-BASED LAB EXERCISE FOR A FIRST THERMOFLUIDS COURSE
Bibliographic record
Abstract
The Thermo-Fluid Engineering I course provides all first-semester second-year engineering students at Dalhousie University with a basic introduction to thermodynamics and fluid mechanics. In the past three years, we have used a combination of six traditional laboratory exercises and a short four-week design project to provide students with hands-on learning experiences in this course. In general, these projects have been well-received by students as a welcome break from the many abstract theoretical concepts that are normally associated with introductory thermodynamics. However, two of the continuing challenges with these projects have been the students’ limited engineering design experience and the availability of time to perform a design project. To address these challenges, in the fall 2015 offering of Thermo-Fluid Engineering I, the four-week design project was replaced by an open-ended design-based lab exercise.The open-ended lab exercise required groups of students to develop specific laboratory experiments related to thermodynamics and fluid dynamics, given a limited quantity of resources. While the focus shifted away from a traditional short design project, the open-ended lab exercise continues to allow students to develop their creative thinking, critical analysis, hands-on, communication, and team work skills, which was the primary purpose of the short design projects in the first place.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".