Generation of personalised e-learning path in adaptive e-learning

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.