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Record W4403764107 · doi:10.24908/pceea.2023.17166

Alumni Perspectives on Engineering Knowledge and Pedagogy

2024· article· en· W4403764107 on OpenAlexaffvenue
Lisa Romkey, Kimia Moozeh, Nikita Dawe, Rubaina Khan, Antonia Barbaric

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsPedagogyEngineering ethicsSociologyEngineering

Abstract

fetched live from OpenAlex

In this work-in-progress research project, the perspectives of program alumni are examined, with the goal of better understanding how this important stakeholder group values engineering knowledge and pedagogy in the large undergraduate engineering program from which they graduated. Perspectives were gathered through extensive semi-structured interviews with alumni of the program, who represented a range of post-graduate outcomes and time since graduation. This paper focuses on alumni perspectives on the following themes, which are of special interest to the program under study: (1) views on the relationship between science and engineering within the context of undergraduate engineering education; (2) perceptions of multidisciplinarity; and (3) beliefs about the role of ethics and social and environmental impact in the program. Understanding the perspectives of alumni, who can connect their experiences forward to their post-graduate working lives, illuminates new perspectives on program design and engineering knowledge.

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)0.010
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0150.007
Scholarly communication0.0130.004
Open science0.0010.007
Research integrity0.0020.005
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 head0.004
GPT teacher head0.215
Teacher spread0.212 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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