Intelligent Syllabus Mapping and Performance Analysis System Using NLP and Neural Networks

The rapid evolution of educational standards requires efficient methods for aligning course content with learning objectives and evaluating student performance. This paper presents an approach for extracting and mapping syllabus content to learning outcomes using Natural Language Processing (NLP) techniques and advanced neural networks. The system integrates text preprocessing, keyword extraction, and web scraping to map course material with predefined learning objectives. A neural-network based performance analysis module further assesses student progress based on mapped content. The proposed system ensures comprehensive coverage of educational standards while minimizing gaps and redundancies. Using NLP and neural networks, the system streamlines curriculum management, enhances personalized learning, and improves educational planning and assessment.