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Record W4327969682 · doi:10.1177/03064190231163487

Active learning approach to enhance rotor dynamics understanding: A classroom demonstration

2023· article· en· W4327969682 on OpenAlexaboutno aff
Lorenzo Bernardini, Egidio Di Gialleonardo

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2023
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorPresentation (obstetrics)Context (archaeology)Process (computing)Rotor (electric)Dynamics (music)Work (physics)Mathematics educationEngineeringTest (biology)Computer scienceMechanical engineeringPedagogyPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.279
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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