PIVOT FOR THE PANDEMIC: COMPARISON OF TOY AND SCIENCE DEMO DESIGN PROJECTS IN A FIRST YEAR MECHANICAL ENGINEERING COURSE
Bibliographic record
Abstract
The first year course, “ME 100: Introduction to Mechanical Engineering Practice, 1”, was redesignedfor the Fall 2017-2019 offerings. The goals of the redesign were to include: a major design project, opportunities for individual communication assessments, and opportunities for development of professional skills. A toy design project was piloted in Fall 2017 as a unifying course theme. In thisproject, industrial partners come to discuss the engineering and design that happens in the toy industry. They also help critique student work as they design a toy of their choosing. With the impacts of COVID 19 the decision was made to pivot to a challenge to design new classroomphysics demonstrations. The course redesign has generally been successful. Both projects have been well received by students, faculty, and industry partners, with students reporting on an end-of-term survey that it was engaging and doable, and that it helped develop their confidence andunderstanding of design, and mechanical engineering. The demo project was generally slightly better received, with 2-8% more students agreeing to statements about the usefulness and appeal of the project. Both projects, the toy project especially, serve as a vehicle to discuss differentaspects of design and professionalism. Challenges exist with giving students guidance at the start and throughout the project to ensure that all student teams have suitably scoped projects. There is also the challenge of helping students develop a design mindset, as several groups struggle with performing the justified decision making necessary to actual design a toy.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".