MétaCan
Menu
Back to cohort
Record W4308911590 · doi:10.24908/pceea.vi.15875

Communication as Design: How a Multimodal Assignment Establishes Communication’s Role in Engineering Design and Provides Stability to a Large Course System

2022· article· en· W4308911590 on OpenAlexaffvenue
Edmund Martin Nolan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)Computer scienceCourse (navigation)Engineering design processFrame problemDesign elements and principlesPerspective (graphical)Management scienceEngineering managementSoftware engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This teaching practice paper describes and reflects on the Engineering Observation, a multimodal communication assignment in a first-year engineering communication and design course. The assignment is designed to accomplish two major goals. First, it fills a pedagogical gap by establishing multimodality and engineering discourse as the foundations of communications instruction and practice in the course, while also establishing communication as an integral part of—and not separate from—design practice. Second, it helps solve problems stemming from the complexity and scale common to large design courses by contributing to the systematic stability of the course. This second goal depends on framing such a course as a system, from the “ecological perspective.” These dual goals are found to be inherently connected, and deliberate care has been given to ensure that they are aligned, mutually supportive, and as effective as possible while ensuring that the assignment supports, and does not negatively impact, connected aspects of the course. Finally, I assess the assignment in its current iteration and consider future directions for the assignment itself as well as this research.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicDesign Education and PracticeFrench-language works237,207