An Augmented Reality Framework for Education: Deep Learning Integration and Impact Evaluation

被引:0
|
作者
Kaviyaraj, R. [1 ]
Mohan, Uma [1 ]
机构
[1] SRM Inst Sci & Technol, Sch Comp, Dept Computat Intelligence, Kattankulathur 603203, Tamil Nadu, India
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Augmented reality; augmented reality in education; AR-based learning tools; deep learning; educational technology; interactive learning environments; YOLO; YOLO;
D O I
10.1109/ACCESS.2025.3551656
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose a novel augmented reality (AR) framework that transforms education with immersive and interactive experiences that enhance student engagement and comprehension. However, existing AR-based learning solutions are built upon generic object detectors that struggle with recognizing domain-specific educational materials, thus limiting their effectiveness. To address this challenge, we introduce a novel fully automated image dataset generation pipeline that synthesizes high-fidelity images from 3D models by varying lighting, camera angles, and background occlusion. This variation enhances the diversity of the dataset, enabling robust training of domain-specific object detectors. The proposed pipeline stands out from existing methods because it provides scalability and adaptability features that allow researchers to build customized educational datasets. The study involved generating a dataset and evaluating it using four state-of-the-art object detection models: Faster R-CNN, SSD, YOLOv5n, and YOLOv7. The YOLOv7 detection model reached an accuracy of 97.2% with 99.5% mAP@0.5 and performed at a real-time speed of 45 frames per second (FPS) making it the best choice for AR applications. To assess the educational impact of our system, we conducted a pilot study involving 210 elementary students. The results showed notable improvements in learning outcomes: fourth graders' scores increased from 68.78 +/- 10.85 to 90.96 +/- 11.70, while fifth graders improved from 62.43 +/- 11.53 to 75.43 +/- 12.53. Comprehensive statistical analyses, including ANOVA, regression, and paired t-tests, confirmed that our approach significantly enhances both academic performance and student engagement when compared to traditional learning methods. These findings demonstrate that our domain-focused data pipeline and optimized object detection framework effectively bridge the gap between deep learning research and AR real-world classroom implementation, offering a highly scalable and transformative solution for AR-based education.
引用
收藏
页码:56067 / 56084
页数:18
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