Active learning approach to enhance rotor dynamics understanding: A classroom demonstration
Bibliographic record
Abstract
In the last decades, novel teaching strategies have been increasingly adopted to improve and enhance the students learning process by promoting their involvement and engagement during classes. In this context, this work presents a laboratory experience proposed to the third-year bachelor students of the course of ‘Mechanics of Vibrations’, held at the faculty of mechanical engineering of Politecnico di Milano. The experience consisted in the presentation of a rotor test bench specifically designed for educational purposes. Main concepts of rotor dynamics were analysed and showed, together with a critical discussion on the discrepancies between the Jeffcott–Laval model and experimental results. This project, that is one of the outcomes of an educational project for post-covid teaching promoted by Politecnico di Milano, involved almost 200 students in total. An anonymous evaluation survey proposed to students revealed a general appreciation of the experience, especially for the possibility of visualising important theoretical concepts. Given the positive feedback, the demonstration will be repeated in the next academic year, with some changes according to students’ suggestions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".