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Incidental Learning in 3D Virtual Environments

2015· book-chapter· en· W4256555295 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

VenueIGI Global eBooks · 2015
Typebook-chapter
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSalience (neuroscience)SalientStyle (visual arts)Virtual learning environmentComputer sciencePsychologyMultimediaHuman–computer interactionCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

With teachers taking more interest in utilizing 3D virtual environments for educational purposes, research is needed to understand how learners perceive and process information within virtual environments (). In this study, the authors sought to determine if learning style or digital literacy predict incidental learning, that is, learning that occurs without learners being instructed to attend to or learn presented information. One hundred and fifty-five education undergraduate students completed a series of tasks in a virtual environment where additional information unrelated to the tasks was present. The results indicate that in addition to incidental learning taking place in virtual environments, learning style and digital literacy seem to predict incidental learning in some instances. The results also suggest that information display, in this case visual salience, plays a role in incidental learning as the participants performed better on recalling information that was made more salient.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.887
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.007

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 head0.037
GPT teacher head0.317
Teacher spread0.280 · 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