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Record W1913384913 · doi:10.24908/pceea.v0i0.3787

COMMUNICATION SKILLS? HOW TO MAKE THEM AN ASSET FOR YOUNG ENGINEERS

2011· article· en· W1913384913 on OpenAlexvenueaboutno aff
Nicole Beaudry, Carole Fisher, Anne-Marie Grandtner, Élisabeth Haghebaert, Jean Brousseau

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationCurriculumContext (archaeology)DisciplineCommunication skillsSkills managementMediationComputer scienceAsset (computer security)ChartTask (project management)Engineering ethicsEngineering managementKnowledge managementPsychologyPedagogyEngineeringMedical educationSociologySystems engineering

Abstract

fetched live from OpenAlex

There is no doubt that engineers need to develop oral and written communication skills during their engineering degree in order to satisfy the expectations of the profession and employers. According to the Canadian Engineering Accreditation Board, the development of communication skills must be part of any engineering curriculum. These cross-disciplinary skills should be integrated throughout the curriculum in order to create links between technical and communication skills. Project-based design courses provide real-world contexts for the development of communication skills. Our actions could benefit from a broader view of communication skills. In this respect, this paper presents a chart structured around four communication situations: reception, production, mediation, and interaction. For each situation, the chart associates a general skill and presents the skill elements, learning objectives, skill activation context, and task samples. The chart can help the Faculty and program or department heads to develop pedagogical strategies and integrate them into specific courses or throughout the curriculum.

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)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.006

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 head0.009
GPT teacher head0.198
Teacher spread0.188 · 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
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

Citations2
Published2011
Admission routes2
Has abstractyes

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