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Назва: Methodology for teaching development of web-based augmented reality with integrated machine learning models
Автори: Semerikov, Serhiy O.
Foki, Mykhailo V.
Shepiliev, Dmytro S.
Mintii, Mykhailo M.
Mintii, Iryna S.
Kuzminska, Olena H.
Ключові слова: web-based augmented reality
WebAR
machine learning
TensorFlow.js
Teachable Machine
educational technology
Дата публікації: 1-лис-2024
Видавництво: CEUR Workshop Proceedings
Бібліографічний опис: Semerikov S. O. Methodology for teaching development of web-based augmented reality with integrated machine learning models / Serhiy O. Semerikov, Mykhailo V. Foki, Dmytro S. Shepiliev, Mykhailo M. Mintii, Iryna S. Mintii, Olena H. Kuzminska // Proceedings of the 11th Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2024) co-located with XVI International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2024). Kryvyi Rih, Ukraine, May 15, 2024 / Edited by: Arnold E. Kiv, Serhiy O. Semerikov, Andrii M. Striuk // CEUR Workshop Proceedings. – 2024. – Vol. 3820. – P. 118-145. – Access mode : https://ceur-ws.org/Vol-3820/paper249.pdf
Короткий огляд (реферат): Augmented reality (AR) is an emerging technology with many applications in education. Web-based augmented reality (WebAR) provides a cross-platform approach to deliver immersive learning experiences on mobile devices. Integrating machine learning models into WebAR applications can enable advanced interactive effects by responding to user actions. However, little research exists on effective methodologies to teach students WebAR development with integrated machine learning. This paper proposes a methodology with three main steps: (1) Integrating standard TensorFlow.js models like handpose into WebAR scenes for gestures and interactions; (2) Developing custom image classification models with Teachable Machine and exporting to TensorFlow.js; (3) Modifying WebAR applications to load and use exported custom models, displaying model outputs as augmented reality content. The methodology is designed to incrementally introduce machine learning integration, build an understanding of model training and usage, and spark ideas for using machine learning to augment educational content. The methodology provides a starting point for further research into pedagogical frameworks, assessments, and empirical studies on teaching WebAR development with embedded intelligence.
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URI (Уніфікований ідентифікатор ресурсу): https://ceur-ws.org/Vol-3820/paper249.pdf
http://ds.knu.edu.ua/jspui/handle/123456789/5505
ISSN: 1613-0073
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