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Record W2764274684 · doi:10.1145/3116595.3116602

The Effects of Navigation Assistance on Spatial Learning and Performance in a 3D Game

2017· article· en· W2764274684 on OpenAlex
Colby Johanson, Carl Gutwin, Regan L. Mandryk

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSerious gameComputer scienceHarmHuman–computer interactionTraining (meteorology)SimulationMultimediaPsychology

Abstract

fetched live from OpenAlex

Navigation in 3D game environments is often difficult for novices, who may get lost and be unable to reach game objectives. Many games provide navigation assistance (e.g., mini-maps, directional markers, or glowing trails); however, there is a risk that players will become reliant on an aid and fail to develop a mental model of the map. To investigate, we carried out two online studies in which people carried out training tasks with varying navigation assistance. After training, they navigated the map with assistance turned off. In both studies, we found that assistance improved training performance, but found no harmful effect of assistance on performance after it was removed, even when comparing between those who received glowing trails to follow and those who received no assistance. We show that navigation assistance in 3D games is effective, and that it does not necessarily harm development of a novice's spatial learning.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.130

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 integrity0.0000.000
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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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

Quick stats

Citations30
Published2017
Admission routes1
Has abstractyes

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