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Record W3112935008 · doi:10.1109/tvcg.2020.3043324

New Media and Space: An Empirical Study of Learning and Enjoyment Through Museum Hybrid Space

2020· article· en· W3112935008 on OpenAlexaff
Farzan Baradaran Rahimi, Jeffrey E. Boyd, Richard Levy, Jennifer Eiserman

Bibliographic record

VenueIEEE Transactions on Visualization and Computer Graphics · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceSpace (punctuation)MultimediaVirtual realityVideo gameHuman–computer interactionLearning environmentEmpirical researchAugmented realityMathematics educationPsychology

Abstract

fetched live from OpenAlex

A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.330
Teacher spread0.279 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2020
Admission routes1
Has abstractyes

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