Personalized Learning Experiences Exploring Adaptive E-Learning Systems for Different Learning Styles

Abstract

Objective: This research aims to provide an overview of the development of adaptive e-learning systems designed based on individual learning style preferences. The main focus is to identify adaptive models, supporting technologies, and challenges associated with the implementation of personalization in learning. Research Design & Methods: This research uses the literature review method by analyzing various scientific articles, research reports, and recent publications related to adaptive e-learning. Findings: This research found that learning style preference-based adaptive e-learning systems have significant potential to improve learning effectiveness by presenting relevant and personalized content. However, its success requires solutions to challenges such as accuracy of preference data collection, privacy protection, and development of flexible and reliable technologies. Implications & Recommendations: E-learning developers are advised to leverage AI for advanced personalization, ensure inclusive design, and implement data protection policies. Further empirical research is needed to test the effectiveness of adaptive models in various learning contexts. Contribution & Value Added: This research contributes to enriching theoretical and practical insights regarding the development of learning style-based adaptive e-learning systems. This review not only offers guidance for educational technology developers but also opens up further research opportunities to address challenges and improve the quality of online learning globally.