Unsupervised and Semi-Supervised Learning Techniques in Contemporary Educational Application

The paper examines the educational application of unsupervised and semisupervised learning techniques.The comprehensive analysis evaluated diverse approaches, methods, and algorithms.Findings indicate that k-means clustering effectively differentiated student performance groups, while dimensionality reduction techniques offered valuable visualization capabilities for complex educational data.The semi-supervised learning paradigm demonstrated particular utility in environments characterized by abundant unlabelled data.The effectiveness of the presented analytical approaches significantly depends on data quality, appropriate algorithm selection, and domain expertise.As educational datasets grow increasingly complex, various computational methods will become essential in developing personalized learning, adaptive educational interventions, and innovative evidence-based teaching practices.The research contributes to the ever-evolving field of educational technology by systematically evaluating the strengths and limitations of various machine learning and artificial intelligence approaches, providing a foundation for future research.