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Record W4391603642 · doi:10.18260/1-2--43768

Game-based and Virtual Reality Sandboxes: Inclusive, Immersive, Accessible, and Affordable Learning Environments

2024· article· en· W4391603642 on OpenAlexaff
Damith Tennakoon, Alexandro Di Nunzio, Mojgan Jadidi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsYork University
Fundersnot available
KeywordsSandbox (software development)HeadsetVirtual realityComputer scienceHuman–computer interactionHaptic technologyGame engineMultimediaImmersive technologyBreadboardSimulationEngineeringSoftware engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Learning complex engineering concepts in varying fields, from learning how to prototype a circuit on a breadboard all the way to learning about the complex geological features that make up well known terrains, require hands-on experience as well as access to sophisticated equipment. In the former situation, many educational institutions can afford lab equipment such as electronic components and large laboratory workplaces. However, there are instances where purchasing expensive equipment for learning is not a viable option. In the latter case, learning about the geological features of a place such as the Grand Canyon is limited to using 2D topographic maps and 3D virtual models; students may not completely comprehend rock strata of terrain through readings and images. We have been developing a fully immersive virtual reality application to tackle these problems; to help students learn through an inclusive, immersive, comprehensive, and accessible environment. The application, called the VR Sandbox, makes use of the Oculus Quest 2 VR headset and the Unity game engine to simulate in-person lab settings and activities. Current developments include an electrical engineering lab where circuits can be modeled and simulated as if the user was in a real laboratory setting. Along with this is a mechanical engineering lab with the activity of assembling a drone and flying it using a radio controller. These tasks are done by wearing the VR headset, which provides a 360° viewing experience, while the handheld haptic feedback controllers are used to interact with the components. The Virtual Reality (VR) Sandbox also provides tours of key national parks such as the Grand Canyon and Swiss National Park, enabling users to visit these locations as if they were there in real life. With these developments, educational intuitions that are not able to afford expensive lab spaces and equipment will have a more feasible option: purchasing low-cost VR headsets for students while still gaining quality educational experience. For a more detailed analysis of terrains, in the field of Earth systems and civil engineering applications, we have been developing another technology called the Virtual Game based Sandbox (VG Sandbox). The VG Sandbox is a web-based computer application, developed using the Unity Game Engine, that teaches users about complex surface terrain model analysis by providing a combination of tools, tutorials, and examples in an intuitive, real-time digital environment. The application includes functionality for measuring horizontal and slope distances, angles, and generate parallel lines between points, visualizing planar structures with the three-point plane problem approach, dynamic topographic line mapping, and generating rivers using a particle-based fluid simulation. To further assist users in their analysis endeavors, each tool also contains a matching tutorial, and in some cases may contain example models to help users understand particularly difficult topics. User experience was of paramount importance; the application provides an intuitive user interface, as well as a robust camera controller for easily navigating the digital terrain model. Additionally, users are provided with functionality for drawing directly onto the terrain mesh itself, saving user progress, and exporting usage statistics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.539

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 science0.0000.001
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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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