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Record W1950634617 · doi:10.24908/pceea.v0i0.5891

Developing Communication Skills during Undergraduate Studies: a Personalized Approach

2015· article· en· W1950634617 on OpenAlexaffvenueabout
Sylvie Hertrich, Dominique Chassé

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAccreditationCommunication skillsClass (philosophy)PortfolioWonderMathematics educationSoft skillsComputer sciencePsychologyMedical educationArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

It is no wonder that communication skills areamong the twelve attributes required by the CanadianEngineering Accreditation Board, since engineers mustpossess strong oral and written communication skills. AtPolytechnique Montréal, all undergraduate students havebeen required to take a mandatory credit for this specific“soft-skill” since 2006.As an alternative to the classical one-term class, thisthree-year long innovative educational strategy is basedon a personalized approach. The first year, after takingdiagnostic tests in written and oral communication,students attend communication workshops. They thenperform realistic communication tasks in engineeringrelatedsituations, both at school and during aninternship. Each year, they complete an e-portfolio andreflect on the development of their communication skills.This method demonstrates the academic andprofessional transversality of a program-based approach.It also allows for practical assessment of complexengineering concepts reflecting the underlying philosophyof the twelve attributes.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.014
GPT teacher head0.232
Teacher spread0.218 · 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 designObservational
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

Citations3
Published2015
Admission routes3
Has abstractyes

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