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Record W2119298906 · doi:10.1017/s0890060410000247

Scrutinizing design educators' perceptions of the design process

2010· article· en· W2119298906 on OpenAlexaff
Megan Strickfaden, Ann Heylighen

Bibliographic record

VenueArtificial intelligence for engineering design analysis and manufacturing · 2010
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDesign processProcess (computing)Engineering design processComputer scienceResearch designDesign educationPerceptionNegotiationVariety (cybernetics)Design briefIterative designDesign methodsNarrativeDesign elements and principlesManagement sciencePsychologyEngineeringWork in processSoftware engineeringSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract When developing a better understanding of the design process there are several possible approaches to choose from. Many studies are based on novice designers (e.g., students) or designers of relatively modest talents. By contrast, some studies have queried designers who are considered to have outstanding and exceptional ability in order to gain an understanding of design at the highest level that it is practiced. The study reported here adopts yet another approach by exploring how design processes are perceived by design educators. The approach is motivated by the observation that teaching design requires consciously distilling the essence of the design process for the students, observing students during their design process and guiding them through the process. As a result, design teachers tend to develop a more articulate view of design processes than most other designers. Nineteen design teachers are interviewed using general topics as discussion points. Such an approach is invaluable when exploring more abstract research questions such as the notion of design processes. This approach differs from more controlled approaches (e.g., protocol analysis) in that it accepts that the data obtained are partially driven by negotiation between the researchers and the participants, and that the discussions are largely stories or narratives about design and designing. The resulting data illustrate that design processes are interpreted, articulated, and understood in a variety of ways by different teachers. These data and subsequent results tell us in rich detail about designing and design teaching, and as a result extend our understanding of the design process.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.285
Teacher spread0.251 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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