Notice bibliographique
Résumé
Abstract The vision and goal of this research project is to use Augmented Reality (AR) technologies for educational purposes within the school system, primarily for K-12 grades. Comprehending Science, Technology, Engineering, and Math (STEM) topics can be hard to perceive specially for students whom are introduced to these scientific fields for the very first time. Based on the small scaled survey that I have created from the responses of rising high school students, all of the participants mentioned that seeing a 3 dimension (3D) model of an object related to STEM subjects will create an interactive and exciting learning experience. They would rather utilize such technology instead of the used methods in today’s classrooms. In the survey, we have focused on 2 dimension photos used in text books and physical models compared to a 3D model where it can be augmented within an app. I have used the AR technology to develop this idea. The research was funded by NASA * Space Grant Consortium and I have pursued this unique software development while being a full time undergrad Information Systems student at university. The main software language used was C# to write scripts inside the Unity software development application to create AR mobile apps. In order for all students to be able to use this app, it needed to be cross-platform therefore same app can be packaged for both Android and iOS devices. I have developed a beta version of this app which enables the user to augment 3D objects on predefined patterns. These patterns are same as Quick Response (QR) codes but the app has been programmed in a way that once the camera of the device detects the pattern within the field of view, the 3D object will be augmented on that pattern. I have printed these predefined patterns on paper and once augmented within the app, these objects will be precisely tracked and locked on the paper. Paper can be moved in every direction and the app will continuously track and place the 3D object on it in real-time. I have presented the results of my research in 3 science fairs and the received responses and feedbacks from the users were amazing. Teachers, students, and parents were all astonished once they saw scientific models such as the DNA helix or chemical molecules being augmented within the app. The user can move the device around these virtual objects and at the same time can fully see every corner of the object as if it was projected in real life. On the other hand, there are lots of technologies used in classrooms every day such as laptops or tablets, however, imagining scientific models can be frustrating or uninteresting for students. I hope that by using AR technologies within the educational system, the students will be interested and inspired to learn more about science and decide to pursue a STEM degree after graduating from high school. * Based on rules of ASEE, to not identify the student.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,018 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».