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Record W2618689334 · doi:10.18260/1-2--10136

Impacts Of The Nserc Chair In Design Engineering At The University Of Manitoba

2020· article· en· W2618689334 on OpenAlexaffabout
Myron Britton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBrainstormingTrilogyClass (philosophy)Computer scienceCurriculumEngineering educationEngineering managementEngineering design processWork (physics)Mathematics educationEngineeringPsychologyPedagogyMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Main Menu design/build projects. Student response has been positive, and we believe these courses provide a sound base upon which to develop design focussed departmental curricula. That same year, in the Department of Biosystems Engineering, two design courses (Introduction to Biosystems Engineering and Design Project) were integrated with a third year class (Design Methods for Machines for Biosystems) to form what we have come to call a Design Trilogy. All three courses are taught in the same time slot and the laboratory sessions are held at the same time, in the same “Design Office” space. Each class retains its own character (see www.umanitoba.ca/faculties and click on Biosystems Engineering under the Faculty of Engineering) but all student design teams are encouraged to work together toward the solution of their increasingly complex industry based design projects. Joint brainstorming sessions and informal discussions lead to significant levels of interaction. All design teams are required to “contract” with students registered in a Trilogy course other than their own to obtain services to complement their own team skills. The objective is to create a simulated design office situation in which students teach one another on a need-to-know basis. The final output from each design team is a written and an oral report, as well as an “invoice” for the work completed. Experience gained in the Trilogy prior to submitting the Design Chair proposal led us to believe that this approach could be applied, with modifications, to all of the programs offered in the Faculty. In July 1999, Dr. Doug Ruth was appointed Dean of the Faculty of Engineering at the University of Manitoba. One of his stated objectives as Dean was to make the University of Manitoba a recognized leader in design education. To provide the necessary Faculty wide support for this goal, he created a new position, Associate Dean (Design Education). In July 2000, the author was appointed to this position. A proposal to NSERC for funding under their Design Engineering Chair program was developed as a means of supplementing the resources needed to reach Dean Ruth’s goal. The University of Manitoba Chair - the proposal The Design Engineering initiative proposed for the University of Manitoba was to have a Faculty wide focus. It responded to all four NSERC targets; training, design and development, collaboration and promotion. It had a proposed schedule, but it was recognized as a design project in itself, and the uncontrollable elements that are characteristic of the design process were recognized as a delivery constraint. Specific components of the proposal included: 1. Improving the design experience base within the faculty. To accomplish this, it was proposed to appoint Engineers-in-Residence. These persons would be drawn from one of two pools of talent within the engineering profession. Recently retired engineers would be appointed as E-i-Rs and located at the university during the academic year. Other engineers would be seconded from industry to provide specific current input during design course laboratory periods. The goal was to appoint at least dozen retired E-i-Rs (two per program) and as many seconded E-i-Rs within the first two years of the program. Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition Copyright © 2002, American Society for Engineering Education Main Menu

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.128

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 head0.012
GPT teacher head0.151
Teacher spread0.139 · 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 designSimulation or modeling
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

Citations1
Published2020
Admission routes2
Has abstractyes

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