BUILDING A MORE COMPLETE DESIGN EXPERIENCE: PHILOSOPHIES AND REFLECTIONS FROM A SECOND YEAR MECHANICAL ENGINEERING DESIGN PROJECT COURSE
Bibliographic record
Abstract
For undergraduate engineering students,earlier exposure to and training in the design engineeringprocess hold much value for an enriched experience andan in-depth understanding of engineering design.Simultaneously, students in their earlier years requiremore guidance and frequent feedback to inform their ownexpectations of learning objectives, as well as developeffective learning strategies. This paper focuses on thedesign and implementation of a second year MechanicalEngineering “Mini-Design Project” course, which hadfour main goals: (1) provide students with their first“complete” design experience, allowing them to take aproject from problem to produced solution; (2) integrateknowledge and skills from other courses in the curriculum;(3) allow for the enhancement of under-represented CEABgraduate attributes, particularly design and teamwork;and (4) prepare students for high performance in theircapstone projects. Several learning needs wereaddressed: Effective teamwork skills, effective projectmanagement, and systematic practice of engineeringdesign with an emphasis on the process. Students wereplaced in teams of 4-5 and given a design problem withspecified evaluation criteria, and strict restrictions onconstruction materials. Students were given milestonesthroughout the term that encouraged them to follow thedesign process, as well as build, test and evaluate theirdesigns. Mechanisms for creating and supporting designteams are described, and students’ feedback andcomments on these mechanism are discussed.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".