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Record W4308912714 · doi:10.24908/pceea.vi.15873

Education As Prototype: On a Combined Architecture-Engineering Design Tutorial

2022· article· en· W4308912714 on OpenAlexaffvenue
Edmund Martin Nolan, Jennifer Davis

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceKey (lock)TeamworkContext (archaeology)Experiential learningArchitectureEngineering educationReflection (computer programming)Knowledge managementEngineering managementEngineering ethicsEngineeringMathematics educationPsychology

Abstract

fetched live from OpenAlex

This article uses expert interviews to support the need for architecture-engineering collaborations in undergraduate education and uses teaching practice reflection to evaluate an example of such a collaboration at work. We establish the state of such collaborations in professional practice and use that as context to consider the design of an undergraduate architecture-engineering collaborative tutorial. We find that while the experiential, project-based educational model employed can mimic key aspects of professional practice, there are limitations to what can be delivered in a one-term experience. Key to understanding those limitations and decisions are the two diagrams provided in the article, which visualize the tutorial and the courses it is attached to as interrelated learning environments. We find that such a tutorial—in addition to delivering core content knowledge to students in each discipline—should create an environment in which students can develop key interdisciplinary skills and abilities, especially as regards communication, teamwork and interpersonal relations. We also reflect on the key design decisions behind the current iteration of the tutorial and identify future considerations for both the tutorial and this research project, of which this article is intended to be the initial stage.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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.

Study designNot applicable
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

Citations3
Published2022
Admission routes2
Has abstractyes

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