Abstract
Over the past two decades, e-learning has transformed traditional educational methods by allowing learners to access content anytime and anywhere.
The rise in popularity of e-learning is due to:
- widespread internet connectivity, powerful computing devices, and software platforms that have opened up digital education to a broader audience,
- cost-effectiveness and reduced costs associated with physical infrastructure, travel, and printed materials,
- scalability of digital platforms that allow for the delivery of education to large numbers of learners without geographic limitations,
- diverse content, including videos, interactive tests, simulations, and games catering to different learners.
The research focuses on developing an automated system for creating a personalized e-learning path in an adaptive learning environment.
The research's target audience is students of vocational and higher education institutions, both first-year and experienced learners with different goals and experiences.
It is planned to use machine learning algorithms for personalization based on existing data from the e-learning platform and learning management systems (LMS) and integrate with existing e-learning platforms for practical application and visualization.
The research objectives are to adapt and organize existing content for personalization using student data and ensure the necessary privacy.