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Record W1873871368 · doi:10.24908/pceea.v0i0.4703

CULTURE AND COMMUNICATION IN THE ENGINEERING CLASSROOM

2012· article· en· W1873871368 on OpenAlexaffvenueabout
Kathleen Clarke

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSession (web analytics)Professional developmentMedical educationPedagogyEngineering educationEngineeringPsychologyEngineering ethicsPublic relationsPolitical scienceEngineering managementMedicineComputer science

Abstract

fetched live from OpenAlex

This session will present successful strategies for instructors and internationally‐educated engineering graduates (IEGs) to manage issues of culture and communication in the engineering classroom with a focus on aspects of the innovative Internationally Educated Engineers Qualifications (IEEQ) Program at the University of Manitoba. The session will also be of interest to those working with international students and mature/adult learners.Several factors influence the successful integration of IEGs into Canadian engineering careers including, but not limited to achieving professional registration and navigating professional, cultural and communication differences. Many IEGs in Manitoba opt to take engineering courses at the University of Manitoba to fulfill the academic requirements for professional registration by enrolling as “special students not seeking degree”, or by completing the IEEQ Program. The classroom itself presents many challenges for IEGs in terms of differences in education systems and academic processes, and like the workplace, the classroom can also be replete with cultural and communication differences, and differences in professional practice. These complex challenges can be time consuming and costly for all parties. Kathleen Clarke will provide a helpful framework of effective practices and lessons learned from the ongoing IEEQ experience in Manitoba.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 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

Citations0
Published2012
Admission routes3
Has abstractyes

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