DESIGN DAYS (RE)BOOT CAMP: INTEGRATING FIRST-YEAR ENGINEERING DESIGN REMOTELY
Bibliographic record
Abstract
Due to the COVID-19 pandemic, course activities pivoted to online learning in 2020. This necessitated a redesign of Systems Design Engineering’s Design Days from a two-day in-person event at the start of term to three shorter online sessions throughout term to prepare and support students during their first term atuniversity. These sessions featured new activities including, an arcade game developed using block coding;a Rube Goldberg virtual ball toss; and the design of a Wi-Fi access point reflector. All activities were designed using principles of effective online learning including, social presence, familiarization with online learning technology, and engaging activities. The online Design Days (Re)Boot Camp ran successfully during Fall 2020, supporting most intended learning outcomes and achieving high initial participation. Continual improvement will focus on sustaining participation, strengthening connections to design and technical concepts, and retaining strong elements upon the return to in-person learning.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".