Making Informed Decisions: EXPLORE Engineering Design ProgramÌ}ƒ
Bibliographic record
Abstract
Decisions must be made at the age of 16 and 17 that can have long-lasting effects. High school students are asked to select a specific degree, a university, and sometimes even a specific discipline with very little basis for making the decision. The EXPLORE program was piloted at Dalhousie University in the Summer of 2014 and 2015 to help girls in high school make an informed decision about whether or not to pursue an engineering degree. 10 students signed up each summer to EXPLORE engineering design in a compressed 2-week schedule where they participated in 3 short design projects, culminating in a major project for a client from the community. The girls developed documentation, presentation, leadership, and teamwork skills. They learned CAD software, practiced 3-D printing, and were exposed to robotic programming. They built and tested a design for a community partner and defended the design to a room of people. The students were introduced to visualization techniques, the engineering design process, log books, and other essential components that they would only otherwise encounter during their first year in an engineering program. This paper will document the elements of the course that help the girls make an informed decision about whether or not to pursue engineering from two perspectives: the instructors' and the student's.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.016 |
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".