Pedagogical strategies to enhance learning and awareness of acoustics within our engineering school community
Bibliographic record
Abstract
Acoustics is taught at the École de technologie supérieure (ÉTS, Montreal, Canada) in a single advanced specialization course during the final year of the mechanical engineering bachelor's program. This course aims to equip students with the skills needed to measure and reduce noise based on the theoretical foundations of industrial acoustics and associated experimental techniques. The fact that the science of acoustics is not well-known among engineering students, coupled with the optional nature of this course, results in an average enrollment of only about thirty students each year (across two distinct sessions), a number further reduced since 2020 due to the unfortunate impact of the pandemic. Paradoxically, Quebec lacks engineers trained in this discipline and often recruits them from abroad. This presentation will aim to showcase various strategies and pedagogical tools that have been used and experimented with in recent years (e.g., flipped classroom, in-class experiments, cellphone measurements, community service-oriented semester projects). The goal is to ensure the quality and enjoyment of student learning and to contribute to raising awareness about acoustics and noise-related issues within the ÉTS community.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".