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Record W2157810541 · doi:10.24908/pceea.v0i0.5848

Engineering Education: Does Our Training Reflect Student Employment Trajectories?

2015· article· en· W2157810541 on OpenAlexafffundvenueabout
James Hewlett, Carolyn Hoessler, Sean Maw

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsCreativityCurriculumCompetence (human resources)CertificateEngineering educationEngineeringEngineering managementMathematics educationComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

Departmental/disciplinary differences aside, newly graduated engineers can be considered to have one of four general and non-exclusive initial employment trajectories: operations, technological innovation, research, and teaching. Survey data from engineering students at the University of Saskatchewan will describe the proportions of students focused on these employment trajectories by year of study, and by discipline. An important implication of this classification is that the desired graduate attributes of these four employment trajectories require divergent knowledge and skills, aside from technical competence. Operations engineers need training in hazard assessment, economics, optimization, schematics, controls, constrained design, and quality control. Technology Innovators require training in creativity, abstract thinking, taking initiative, open-ended design, technical graphics, prototyping, and market research. Research engineers need training in experimental design, statistics, the scientific method, programming, instrumentation, and data analytics. Teaching engineers require training in pedagogy, communications, curriculum design, and social-media tools. All Canadian engineering schools train for Operations. Most have an option/certificate/specialization for Technological Innovation. Some have a minor emphasis on training for the Research stream. Very few systematically prepare for the Teaching role. Are we losing some good engineers by lack of curricular support for these latter three aspirations? Equally important, are sufficient numbers of engineers being prepared in each trajectory? These questions will also be addressed in this study, as data reflecting on the personality characteristics of student respondents was collected and analyzed while looking at their employment trajectories. The potential implications of this type of analysis on attrition and retention, innovation in Canada, and more effective teaching of STEM, will be

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)0.005
metaresearch head score (Gemma)0.031
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations5
Published2015
Admission routes4
Has abstractyes

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