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Record W2242896298 · doi:10.18260/1-2--7200

An Integrated Course On The Experimental Method In Engineering

2020· article· en· W2242896298 on OpenAlexaffabout
Yvan Champoux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCurriculumSession (web analytics)Computer scienceCourse (navigation)Class (philosophy)Mathematics educationEngineering educationEngineering managementEngineeringArtificial intelligencePedagogyWorld Wide WebMathematicsPsychology

Abstract

fetched live from OpenAlex

In 1996, the Mechanical Engineering Department of l'Université de Sherbrooke introduced a new and progressive curriculum.A course entitled "Experimental Method in Engineering" was developed to teach to the students how to solve technical problems using an experimental approach.The course was offered for the first time in the fall of 1997 to a class of 120 sophomores.The purpose of the course is threefold.First, it covers the basic knowledge associated to experimentation.Second, several laboratories are used to enhance the understanding of the courses content and to develop the students skills.Finally, the course is closely linked to a major semester experimental project.This paper presents a short description of the course content and how the course was designed.It also demonstrates that the course is an excellent "integrator" that allows the students to link the knowledge covered in various courses.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.094
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0940.022

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 head0.268
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2020
Admission routes2
Has abstractyes

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