DESIGN DAYS BOOT CAMP: ENHANCING STUDENT MOTIVATION TO START THINKING IN ENGINEERING DESIGN TERMS IN THE FIRST YEAR
Bibliographic record
Abstract
Abstract – Engineering design is a core aspect of engineering education. Students might not appreciate the importance of engineering design early on, especially in the first term of their academic study. In this paper, we propose an approach for motivating students to think early about engineering design, namely at the start of their first academic term. The approach entails organizing an immersive design boot camp –“Design Days” – that replaces lectures for the first two days of the term. During the two days, students are divided into randomly assigned teams, and provided specifications to work on a design challenge. As part of the challenge, students are incentivized to fully understand the problem before attempting to solve it, instructed to follow a design process and apply iterative design, and asked to document their design and its rationale. The proposed method was successfully applied in Fall 2016. The approach will be applied again in Fall 2017 with minor modifications based on student feedback.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".