Pilot Study Results from Using TrussVR© to Learn About Basic Trusses
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».