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

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

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

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineComputer Science
ThématiqueAugmented Reality Applications
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésSandbox (software development)HeadsetVirtual realityComputer scienceHuman–computer interactionHaptic technologyGame engineMultimediaImmersive technologyBreadboardSimulationEngineeringSoftware engineeringElectrical engineering

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,977
Score d'incertitude au seuil0,539

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,273
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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