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Applications of Extended Reality Technologies within Design Pedagogy: A Case Study in Architectural Science

2021· article· en· W3194071623 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal for Digital Society · 2021
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceEngineering ethicsArchitectural engineeringMathematics educationHuman–computer interactionSociologyEngineeringPsychology

Abstract

fetched live from OpenAlex

The concept of virtualizing and augmenting realities through technology has evolved from fantasy to feasibility and has advanced how humans are able to visualize and interact with the digital world. Extended realities (XR) often interpret threedimensional space in both realistic and conceptual forms, leveraging the ability of macro and micro scaling of computerized images. The versatility of VR is used in a wide range of disciplines from creative industries to professional practices and as an interactive multi-sensory visualization medium, it can be effectively adopted as a learning tool, used to elevate the experience in the classroom. This paper examines the possibilities of the incorporation of virtual reality, augmented reality, and mixed reality into the post-secondary architectural academic setting through lecture-based education, design pedagogy, project feedback delivery, and enhancement of experiential learning. The paper provides a case study of implementation into models of pedagogy at Canada's largest architecture program, in order to enhance the learning experience both within in-person and online learning contexts.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.050
GPT teacher head0.390
Teacher spread0.340 · 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