Smart Education and Sustainable Learning Leveraging Soft Computing Techniques for Efficient Duplicate Question Detection

Abstract

Smart education systems are revolutionizing learning by integrating advanced computational methods that enhance student engagement and streamline educational processes. One significant challenge in online learning platforms is the redundant occurrence of duplicate questions, which not only wastes resources but also hampers the effectiveness of personalized learning. This book chapter explores the use of soft computing techniques, such as fuzzy logic, neural networks, and genetic algorithms, to detect and eliminate duplicate questions in smart educational environments. By leveraging these intelligent methods, the chapter provides insights into how sustainable learning can be achieved through efficient data management, reducing redundancy and improving the overall learning experience. The proposed models enhance the adaptability and scalability of educational platforms, promoting efficient content delivery, better resource utilization, and improved learner outcomes.