Adaptive Visual Learning Using Augmented Reality and Machine Learning Techniques

Abstract

The current curriculum forces students to understand topics by visualizing axonometric structures in their cognitive minds depending upon the conceptual texts and information. This methodology is inconsistent as the idea of visualization through conceptual knowledge is dependent on the level of reasoning and IQ (Intelligence Quotient) a student possesses. It is usually common for a student to misinterpret an information due to lack of reasoning and imaginative skills. Our educational model aims to diminish this intellectual barrier by incorporating Augmented Reality (AR) and Machine Learning (ML) techniques together and create an Adaptive Visual Learning experience for students. A mobile interface with OCR (Optical Character Recognition) and TTS (Text-To-Speech) feature is given to make this whole process simple and easy to use for any student. In this paper, two ML techniques Logistic Regression and Neural Network are applied in order to enhance and modify the existing educational system by removing the intellectual barrier involved due to neurodiversity. A comparative study is performed between the two ML algorithms, where in Logistic Regression performed better than the Neural Network. This form of adaptive visual learning aims to boost student performance in academia.