Bibliographic record
Abstract
Providing hands-on laboratory experience, can be an important part of engineering programs. However, hands-on learning opportunities can be very difficult to implement in online learning environments. This work discusses the implementation of a solid mechanics laboratory investigation challenge that was developed for students to conduct in their own homes. To ensure that the laboratory was feasible, the materials and measurement equipment utilized had to be readily available and affordable. For this reason, the investigation involved the analysis and structural optimization of a foam board part. Students cut the partgeometry out of foam board, applied a load to the part using a water filled weight and optimized the design to reduce the total volume of the part while still supporting the required load. Students utilized digital imagecorrelation software to experimentally investigate the full field strain when the load was applied to the foam board part. Students optimized the part geometry using analytical calculations and finite element simulations. This approach resulted in a very positive response from the students and an excellent quality of learning was demonstrated by the students through their reports. This approach could be beneficial in other online courses. Furthermore, this experience has demonstrated the benefits of giving students greater responsibility for their own laboratory experiences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.062 |
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".