Pilot Study Results from Using TrussVR© to Learn About Basic Trusses
Bibliographic record
Abstract
TrussVR © , a custom-designed VR application, was developed to help engineering students learn about basic trusses in a virtual lab environment.Trusses are a mainstay of many first-year engineering Statics courses.They are relatively simple to analyze.However, hand calculations are typically time-consuming.As a result, most textbook problems involve evaluating one loading scenario and end with the calculated values of forces running through the truss's twoforce members (2FMs).This scenario does not lend itself to a holistic understanding of how trusses behave under loads of various magnitudes and locations.It does not facilitate a comparison of the relative strengths and weaknesses of different truss designs, nor a constructivist learning style driven by curiosity.TrussVR © carries out the computations of solving a truss almost instantaneously.What this affords is a new way to learn about trusses, and a way to learn features of trusses that have been previously impractical to learn through conventional lab techniques.Build a truss, apply an external force, and see the distribution of forces within the truss.This cycle can be repeated quickly in VR, allowing learners to gain an enhanced visual appreciation of how trusses behave.In the 2018/19 academic year, 166 first-year engineering students at the University of Saskatchewan volunteered for a pilot study that examined the efficacy of TrussVR © .The study was approved by the U of S Behavioural Research Ethics Board.Volunteers completed a "presurvey" that examined knowledge, skills, and attitudes regarding trusses, before trusses had been taught in the classroom.Participants were then randomly assigned to one of three groups: no lab, regular lab, and TrussVR © lab.All participants then attended 2 weeks of in-class lectures on trusses, frames, and machines, as they normally would.During this time, the "no lab" group did not complete an experimental lab on trusses.The "regular lab" group completed the same 3-hour lab as the rest of the course's non-participants.This involved the evaluation and construction of a physical truss using a Pasco © construction set.The "TrussVR © " group went through a 7-10 minute tutorial and then a 20 minute VR lab experience consisting of i) examining and playing with a variety of truss types, ii) solving a number of skill testing problems involving basic trusses, and iii) building a bridge and testing it.At the end of the 2 week unit on trusses, frames, and machines, participants in all groups completed a mixed-methods "post-survey" that reexamined their knowledge and skills, as well as their impressions of the TrussVR © and Pasco © systems, if they used one.A "distant post-survey" was conducted two months later followed by a "very distant post-survey" another seven months after that, to further re-examine knowledge, skills, and attitudes.Statistically significant differences were found between TrussVR © and the other groups (p < .01)for recognition and recall of truss types two months and nine months afterwards.Likewise, the virtual lab experience was highly rated in most respects.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".