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Record W1925677124 · doi:10.24908/pceea.v0i0.3900

UNIVERSITY AND COMMUNITY COLLEGE STUDENTS CAN SHARE A LEARNING EXPERIENCE

2011· article· en· W1925677124 on OpenAlexaffvenueabout
Barrie Jackson, Dale Dilamarter, Peter Spasov

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's UniversityFleming College
Fundersnot available
KeywordsPresentation (obstetrics)Multidisciplinary approachThe artsEngineering educationEngineeringMedical educationQueen (butterfly)Engineering managementPsychologyManagementSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This paper describes a pilot collaboration between Queen’s University and Sir Sandford Fleming College of Applied Arts and Technology in Peterborough Ontario. Since 1994 Queen’s has offered projects where students learn by solving problems for fee paying industrial clients. Known as Technology Engineering and Management (TEAM) student participants form multidisciplinary teams to consult for business clients. In addition to engineering students, commerce and arts students have often participated in the teams. In the Applied Projects program at Fleming College, third year engineering technology student teams solve problems for enterprise sponsors. A pilot group of engineering technology students from Fleming College worked with students in two Queen’s University TEAM projects. In industrial practice, engineers and engineering technologists often collaborate on solving problems. This collaboration rarely occurs in an educational setting. In the 2002-2003 academic year the pilot exercise simulated the professional working relationship between engineers and technologists. This paper gives a description of the experience and the motivation to undertake this unique collaboration. The most important aspect of the presentation is a critical assessment of the University/College collaboration -- what worked, what problems arose, and what improvements are suggested.

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)0.000
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 score0.057
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations0
Published2011
Admission routes3
Has abstractyes

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