Providing Meaningful Hands-on Design Experience in the Remote-learning Environment with a Miniature Mechanical Testing Kit
Bibliographic record
Abstract
Laboratory work teaches students how technical knowledge is applied in practice and has long been recognized as a crucial component of a complete undergraduate engineering experience.Due to the COVID-19 pandemic, many engineering programs have been unable to provide these traditional hands-on activities in the remote learning environment.To address this challenge within our program at the University of Toronto, a low-cost, open-source miniature mechanical testing kit (MMTK) was designed and deployed in a third-year engineering design course.Students in their junior year in this course were delivered the kits and were responsible for assembly, sample testing, as well as designing and performing experiments using the MMTK.Students were surveyed regarding their perceptions of the activities.Results showed the MMTK was a useful tool that provided students with a unique hands-on experience during the pandemic.Activities with the MMTK have generally increased students' confidence with hands-on work and conducting experiments.Future work will include further development of the MMTK for use more broadly within engineering research and education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".