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Record W3036414253 · doi:10.24908/pceea.vi0.14121

INVESTIGATING STUDENT EXPERIENCES OF ENGINEERING TECHNOLOGY TO ENGINEERING TRANSFER IN ONTARIO

2020· article· en· W3036414253 on OpenAlexaffvenueabout
Hannah Smith, Brian Frank

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsQueen's University
Fundersnot available
KeywordsAccreditationGraduation (instrument)Technology transferEngineering educationMedical educationEngineering researchBridging (networking)EngineeringEngineering managementComputer scienceMedicineKnowledge managementMechanical engineering

Abstract

fetched live from OpenAlex

The Ontario Council on Articulation and Transfer (ONCAT) Engineering Pathways Project aims to develop a large-scale network for transfer between engineering technology and engineering programs, enabling a streamlined transfer experience across multiple Ontario institutions. When developing a program of this nature, the lived experiences of students who have completed such a transition must be investigated. Eight interviews were completed with students and graduates of Ontario engineering technology to engineering transfer programs, with the intent to understand a) rationale for student transfer, b) experiences while studying, and c) experiences or plans post-graduation. A phenomenological methodology was used for analysis by inductive coding. Major results include the necessity of program accreditation for student enrollment, the benefits of short and rigorous bridging programs, and the marked lack of social and academic support students experienced during their transfer pathways. This research has been used to inform the development of the pilot phase of an Ontario-wide engineering transfer program.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.300
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.015
GPT teacher head0.263
Teacher spread0.249 · 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 teacher head, 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

Citations3
Published2020
Admission routes3
Has abstractyes

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