Advanced machine learning techniques for personalising technology education

Abstract

This review paper explores the intersection of advanced machine-learning techniques and personalised technology education. It examines how machine learning models can be leveraged to tailor educational content and teaching methods to individual learning styles and needs, focusing on adaptive learning systems and intelligent tutoring systems. The paper discusses challenges associated with implementing machine learning in education, including data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access to personalised learning. Future research directions and strategies for overcoming these challenges are proposed, highlighting the importance of improving data quality, developing ethical guidelines, promoting educator training, and fostering stakeholder collaboration. Personalised technology education can enhance student empowerment and equal access to high-quality education by tackling these issues and adopting moral values. Keywords: Machine Learning, Personalised Education, Adaptive Learning Systems, Intelligent Tutoring Systems, Ethical Considerations, Educational Technology.