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Record W2744876291 · doi:10.18260/1-2--13515

Fundamentals Of A First Year Engineering Design And Communication Course: Familiarization, Functionality And Testing

2020· article· en· W2744876291 on OpenAlexaffabout
Daryl Caswell, Clifton R. Johnston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEngineering design processGRASPSession (web analytics)Process (computing)Computer scienceDesign educationEngineering educationSoftware engineeringEngineeringEngineering managementWorld Wide WebMechanical engineeringVisual arts

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1793 Fundamentals of a First-Year Engineering Design and Communication Course: Familiarization, Functionality and Testing D.J. Caswell, C.R. Johnston, O.R. Fauvel, D. Douglas, M. Eggermont Department of Mechanical & Manufacturing Engineering/Faculty of Engineering, University of Calgary Calgary, Alberta, CANADA Introduction There is a large body of pedagogy surrounding the use of a common design methodology in engineering. The well known description of the design process as a series of steps from problem identification, through conceptual design to detailed design has been a standard in engineering design courses of all levels for at least 50 years. The design process does indeed document the main components of successful design. However, the ability of engineering students to grasp the significance of the steps in the design process is not ideal. In fact, it might be said that employing the standard design process is much like playing the music of Mozart: Too hard for professionals but too easy for students. Even at the professional level, blind adherence to a set procedure for design too often results in the creation of inferior products6 Much of the literature describing the loss of engineering design skills, particularly in North America, focuses on the ways in which the process might be given meaning for the students of engineering design. It is our view that the great number of pedagogical devices, case studies and design projects that have been developed over the years to demonstrate the application of the traditional design process (with sporadic levels of success) is an indication that there may be a problem with the portrayal and implementation of the design process itself. In short, the design process makes sense when taking a retrospective view of a successful design. However, it breaks down when a novice attempts to apply the process to a real design project. For example, the first design process step of determining the problem is known by every designer to be the most difficult part of design and often occurs closer to the end of the actual design process than the beginning. We have taken on the task of developing an approach to the design process that we refer to as the “Design Trinity” of Familiarization, Functionality and Testing. The design trinity, employed in an environment of participatory inquiry 13 is intended to engage the novice designer in a way that develops a sense and rigour to their design work. It is intended to occupy a mid-point between the “man-on-the street” approach (an endless cycle of build/fail) and the highly sophisticated use of successful design methodology. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society for Engineering Education

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.099
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0990.064

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 head0.053
GPT teacher head0.243
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations13
Published2020
Admission routes2
Has abstractyes

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