An Immersive Hybrid Approach to Materials & Solid Mechanics Lab Activities for Undergrad Students
Bibliographic record
Abstract
This paper describes two institutions' efforts to provide engineering science students with experiential learning opportunities using low-cost, simple physical lab experiments and its efficacy in improving students perceived understanding levels. A Canadian university developed a "hybrid" lab activity that combined a virtual lab simulator with physical lab experiments to teach materials, solid mechanics, and instrumentation concepts in two different 2nd year undergraduate solid mechanics courses. At a small American college, students in an introduction to materials engineering course completed four individual laboratory exercises using simple and relatively inexpensive material testing setups that explored topics covered in course lectures and readings. Students learned about the behavior of engineering materials and structural analysis using low-cost materials test apparatuses for different loading modes, engaging their senses to aid their understanding. Students then constructed a virtual simulation model using Finite Element Analysis (FEA). Students at both institutions gave positive feedback and reported improved understanding of course topics. The use of low-cost experiments combined with traditional engineering labs shows promise for improving student understanding of engineering science concepts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".