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Record W3001176619 · doi:10.24908/pceea.vi0.13715

GATHERING THE VOICE OF THE STUDENTS FOR ACCREDITATION PURPOSES THROUGH THEIR DEFINITION OF “ENGINEER”

2019· article· en· W3001176619 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAccreditationWarrantComputer sciencePerceptionPopulationGraduate studentsMedical educationMathematics educationEngineering managementPsychologyEngineeringPedagogyMedicine

Abstract

fetched live from OpenAlex

This paper proposes a novel indirect assessment method to capture the voice of the students for program accreditation purposes. It consists of asking students, individually and then in teams, to draw up a list of keywords they associate with being an engineer and to write a formal definition of engineer. The raw data (list of keywords and definitions) is closed coded for the twelve graduate attributes (GAs) defined by Engineers Canada. First-year, mid-program and last-year students participated in the study in order to verify change of perception as students advance through the program. Results are compared for individual and teams, as well as for the different student populations. Sufficient insight into the program’s contribution to the development of graduate attributes in its student population (or apparent lack thereof), information that can be used for continual program improvement, was gained to warrant internal validity of the method.

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)0.000
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 score0.504
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.008
GPT teacher head0.205
Teacher spread0.198 · 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