Communication as Design: How a Multimodal Assignment Establishes Communication’s Role in Engineering Design and Provides Stability to a Large Course System
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".