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3D Video Production in Education

2013· book-chapter· en· W2496615063 on OpenAlexaff
Jay Wilson, Stefan Scott

Bibliographic record

VenueAdvances in early childhood and K-12 education · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVideo productionVideo editingDigital videoMultimediaComputer sciencePost-productionSoftwareProcess (computing)Production (economics)Non-linear editing systemVideo captureVideo recordingInteractive videoVideo processingSmacker videoArtificial intelligence

Abstract

fetched live from OpenAlex

Use of video by teachers can make learning more engaging or content easier to understand. Video shown in classrooms can be created by others and adapted by or created by the teacher. Traditionally, video created by teachers can be produced with inexpensive digital cameras and basic editing software. With a renewed commercial interest in Three-Dimensional (3D) video and with video equipment costs declining, the option now exists to have 3D video in classrooms. What is not known is the process to allow for teacher-created video that can take advantage of the 3D format. This case details how an undergraduate teacher candidate created a 3D video using existing production equipment and freely available software. The chapter examines the planning details, course design, and outcomes related to 3D video production. Suggestions to educators for integrating 3D into their schools are also included.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.003
GPT teacher head0.205
Teacher spread0.202 · 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 teacher head, not a consensus.

Study designOther design
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

Citations0
Published2013
Admission routes1
Has abstractyes

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