Enhancing Geometry Education through Deep Learning Models: Addressing Challenges in Three-Dimensional Shape Visualization

Abstract

Integrating technology into mathematics education is crucial for enhancing the understanding of mathematical concepts and skills, as well as increasing motivation. This is particularly applicable in geometry classes, where technology can facilitate the detection of geometric shapes, impacting both the learning and teaching processes. In this context, emerging concepts of artificial intelligence and deep learning can be utilized as tools to overcome such limitations. This study addresses the challenges that teachers face when drawing three- dimensional geometric shapes in digital environments. Shapes drawn manually in digital environments can often be complex, making it difficult for teachers to create accurate and precise drawings. Deep learning models can assist teachers in correcting drawing errors, thereby providing students with clearer and more comprehensible visuals to facilitate the learning of geometric concepts. The study emphasizes the high accuracy rates achieved using various deep learning models, highlighting their impressive capabilities in accurately classifying geometric shapes.