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Record W2139732628 · doi:10.24908/pceea.v0i0.3911

ALUMNI/AE SURVEYS AS TOOLS FOR DIRECTING CHANGE IN ENGINEERING CURRICULUM

2011· article· en· W2139732628 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsCurriculumSet (abstract data type)Medical educationEngine departmentSurvey researchEngineeringEngineering managementPsychologyPedagogyComputer scienceMedicineApplied psychology

Abstract

fetched live from OpenAlex

Like any other similar department, the Department of Mechanical and Materials Engineering (MME) at Queen’s University adapts the undergraduate curriculum on an ongoing basis using input from students, faculty, alumni/ae and other sources. Of particular usefulness have been alumni/ae survey results. These results have allowed priorities for curriculum changes to be set with the confidence that graduates will be well equipped to meet the demands of a changing workplace. This paper describes the results of the most recent survey. Many of the suggested changes to the curriculum have already been implemented. One of the ongoing challenges of the Department will be to repeat the survey regularly in the future, to see if the current changes made a difference in terms of employer and graduate satisfaction.

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.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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