Enhancing Learning Style Identification through Advanced Machine Learning Techniques Text Analytics of Models and Applications in Personalized Education
Keywords:
Machine learning (ML), Learning styles (LS), Convolutional neural networks (CNNs), Personalized education, Educational technologyAbstract
The advent of enhanced machine learning (ML) techniques has revolutionized the identification of learning styles (LS) in educational settings, offering more accurate, scalable, and personalized approaches compared to traditional methods. This study reviews recent advancements in ML models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble techniques for LS detection. These methods have been shown to improve teaching strategies by providing educators with data-driven insights into student preferences, ultimately enhancing engagement and academic performance. However, challenges such as data privacy, ethical considerations, and model interpretability continue to hinder broader adoption. This paper also discusses future research directions, including the integration of ML with emerging technologies such as augmented reality (AR) and virtual reality (VR), and its application in diverse educational contexts. The findings suggest that enhanced ML techniques offer a promising avenue for advancing personalized education while addressing current limitations.
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