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Record W4405764992 · doi:10.24908/pceea.2024.18470

Assessment of the students’ self-efficacy regarding the CEAB graduated attributes

2024· article· en· W4405764992 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) · 2024
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPsychologyMedical educationMathematics educationApplied psychologyMedicine

Abstract

fetched live from OpenAlex

To meet the accreditation criteria set forth by the Canadian Engineering Accreditation Board, institutions are required to provide evidence that their graduates exhibit proficiency in 12 specific attributes. These attributes are usually assessed by instructors, albeit with some reliability and validity concerns. In an effort to enhance the variety of data sources, a study has been initiated to measure students' perceived sense of competency in relation to these graduate attributes. To assess their self-efficacy, 207 first to fourth year undergraduate students of sixteen courses were asked to voluntarily respond to a survey during the last three weeks of the fall 2023 semester. The statistical analysis of the gathered data reveals that students' self-efficacy is the lowest toward the graduate attribute 9 related to the impact of engineering on society and the environment. Therefore, a program improvement is recommended to enhance the students’ self-efficacy regarding this graduate attribute.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.301
Teacher spread0.289 · 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