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Record W2246196382 · doi:10.18260/1-2--19232

Assessing the design of a rapid product design cycle activity that develops student understanding of engineering design and professional practice

2020· article· en· W2246196382 on OpenAlexaff
Patricia Sheridan, Robert Irish, Jason Foster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEngineering design processDeliverableConceptual designProduct designDesign review (U.S. government)New product developmentDesign educationTeamworkEngineeringEngineering managementDesign technologyKnowledge managementComputer scienceSystems engineeringProduct (mathematics)Operations managementHuman–computer interaction

Abstract

fetched live from OpenAlex

This paper analyzes the efficacy of a rapid, interdependent design sequence on student learning and engagement. The Rapid Product Design Cycle (RPDC) activity takes students through a three-part waterfall design sequence -problem formulation, conceptual design, and detailed design. Our objective was to give the students an appreciation of the challenges faced by interdependent teams across multiple different design stages within tight time constraints, and to encourage design work under the constraining pressures of time and stakeholder expectations. This paper first details the design of the RPDC activity, and then examines the administration and logistics, assessment, student engagement and learning, and student response to this highly accelerated product design cycle. The examination of the activity pays specific attention to the challenges posed by a high frequency of cognitive disruptions (3 different design tasks in 5 weeks) compounded by the requirement of working in small teams.

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)0.002
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.166
GPT teacher head0.351
Teacher spread0.185 · 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
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

Citations1
Published2020
Admission routes1
Has abstractyes

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