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Record W4240324802 · doi:10.18260/1-2--35055

Pilot Study Results from Using TrussVR© to Learn About Basic Trusses

2020· article· en· W4240324802 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTrussComputer scienceStaticsCuriosityMathematics educationStructural engineeringEngineeringArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.288
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it