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Record W2600348831 · doi:10.18260/1-2--16905

Design Of An Instrument To Assess Understanding Of Engineering Design

2020· article· en· W2600348831 on OpenAlexaff
Kristen Facciol, Lisa Romkey, Jason Foster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapstoneEngineering educationCurriculumDisciplineMultidisciplinary approachEngineering design processRubricEngineeringEngineering managementEngineering ethicsComputer scienceMathematics educationMechanical engineeringPedagogyPsychology

Abstract

fetched live from OpenAlex

Engineering design education is an important element of any undergraduate engineering curriculum.It is also an element undergoing constant evolution, reflecting the rapidly evolving needs of engineering industry and academia.Engineering graduates are expected to contribute effectively as members of multidisciplinary engineering design teams.Enabling this success requires that engineering design educators develop an understanding of the diverse disciplinary perspectives on engineering design and of the evolving perspectives of their students.This paper first describes the disciplinary perspectives that emerged as a result of some preliminary research on engineering design education, and then describes the development of an instrument for evaluating individual understandings of engineering design.Disciplinary perspectives were explored through interviewing the instructors of four capstone design courses in different engineering disciplines within a large engineering Faculty.Each instructor was asked about their instructional history, the requirements and expectations of graduates from their respective engineering undergraduate program, and their past attempts to understand course outcomes.Although instrument testing is still required, the instrument developed can be presented to a group of students at the beginning, mid-stream and completion of their capstone design course.It can also be used to track changes in students' perceptions, as well as the influence that a particular discipline may have on an individual's understanding of engineering design.Course instructors will then be able to identify which aspects of their courses are most influential and which require more development.Recognition of the design methodologies and expectations within specific engineering disciplines is an important first step in developing a curriculum that enables engineers to work across those disciplines.An instrument that supports the analysis of a Faculty's progression towards this end is a valuable addition to the engineering design educator's toolbox.

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)0.038
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.038
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.120
GPT teacher head0.250
Teacher spread0.131 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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