Leveraging Semantic Analysis in Machine Learning for Addressing Unstructured Challenges in Education

Abstract

The present study explores the role of semantic analysis in machine learning for addressing unstructured challenges in education. Through a comparative analysis and literature review, various semantic analysis techniques and machine learning algorithms were investigated, examining their effectiveness and the factors influencing their success in educational contexts. The findings demonstrate that advanced semantic analysis techniques, such as word embeddings and deep learning-based approaches, significantly improve the performance of machine learning algorithms in processing unstructured data, leading to better natural language understanding and more accurate insights from educational data. Factors such as data quality, algorithmic complexity, and computational resources play a crucial role in determining the success of semantic analysis-based machine learning models in education. The study concludes with recommendations for further development and application of semantic analysis and machine learning in education.