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Record W2284422316 · doi:10.31686/ijier.vol4.iss1.501

Understanding and Creating 3D Forms Using Familiar Objects

2016· article· en· W2284422316 on OpenAlex
Mithra Zahedi

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

VenueInternational Journal for Innovation Education and Research · 2016
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStudioProcess (computing)Mathematics educationVocabularyComputer scienceSpatial abilityExpression (computer science)Subject (documents)PsychologyWorld Wide WebLinguistics

Abstract

fetched live from OpenAlex

A fundamental for first-year design students is to express ideas by drawing and creating volumetric models. Traditionally, this education includes spatial geometry and generation of forms whereby students learn to appreciate intersections of volumes and projections to describe three-dimensional (3D) forms in two dimensions. However, given the aptitude of today’s students to operate 3D-modelling software and the general accessibility of current technology, spatial geometry as a core subject may seem less relevant. Our goal is to re-engage students in learning required basic knowledge and skills through a complex multifaceted design process. We have designed a first-semester course of four project-based learning activities that apply learning-by-doing methodology. For each of the past three years, 65 to 75 students have participated in our 3D Expression studio course, in which they develop understanding of design process, vocabulary, and skills to create 3D models with precision, refinements, and high-level visual impact. This paper reports on the successful results of activities conducted during the 14 full days of this studio course.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.309
GPT teacher head0.477
Teacher spread0.168 · 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