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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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
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 teacher head, not a consensus.

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

Citations2
Published2011
Admission routes2
Has abstractyes

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