Remote Delivery of an Introductory Architectural Engineering Design-Build Activity
Bibliographic record
Abstract
Abstract The first Architectural Engineering (AE) class at the University of X (UX) began in fall of 2018. The mandatory co-op work experience, studio component each semester, and collaboration with the UW School of Architecture are features of the program that make it unique in North America, just to name a few. In order to provide an introduction at the beginning of the school year that would adequately capture the essence of the program, a tried-and-true hands-on engineering project model at UX called 'Design Days' was adapted for the AE program. In 2018, the inaugural two-day design-build project called 'AE Design Days' was held wherein first year students worked in groups to design a piece, or set, of furniture that enhanced an assigned site in a UX Engineering building. The objectives of the project were to provide an 'ice-breaking' opportunity between students, as well as with the faculty; introduce the students to the AE course content, especially as it relates to the design process; provide opportunities for the students to work with their hands building models; and, to allow for the course instructors to gauge the skillset and prior knowledge of the incoming students[1]. Following the success of the first AE Design Days event, the same project model was implemented 2019, with minor modifications to improve the event logistics and student experience. Following the transition to online learning as a result of the COVID-19 pandemic, a dramatic overhaul of the event was required to hold the most recent iteration in the fall 2020 semester. The intention was to have the same objectives for the project; however, with the students learning entirely remotely, and most doing so from home, some objectives were emphasized (e.g. student interaction). The event objectives were set to prime the students for the project-oriented courses in the AE program, through aspects inductive and experiential learning, and combined with an effort to address the specific challenges of online learning. The online format of the event lent several constraints, as it would not be possible to build models to the extent that it had been done in previous years. This paper discusses the planning process and implementation of the virtual AE Design Days event, as well as the perceived challenges of online learning within Architectural Engineering, but also the successes of the event from both the student and instructor perspective.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.030 |
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".