Personalized Recommendation of Educational Resource Information Based on Adaptive Genetic Algorithm

The abundance of online educational resources has made it increasingly difficult for students to identify the correct learning materials in recent years. Overcoming the information overload that has emerged in the new education systems is possible via a tailored recommendation system. It encourages students to look for new ways to get around the subject matter and to use information from all across the world. Because of this, many academics are working to create learning systems that incorporate methods for creating a unique learning experience for each user. Therefore, our proposed approach was to create an appropriate learning route for each student, and they are using Educational Resource Information Based on an Adaptable Genetic Algorithm (ERI-AGA). Evidence from studies shows that the suggested technique can provide relevant course materials for students based on the specific needs of students to help them study better in a Web-based system. Personal recommendation engine, pre-processing and learning-based model development, and implementation of the recommendation system will be researched. Participatory budgeting PB-level data storage and processing as well as the ability to suggest in real time will be studied. The capacity to make real-time suggestions and the storing and processing of PB-level data will be investigated. It was critical to check the system's availability by running associated tasks and performance tests. The comparison values demonstrated that ERI-AGA was a reliable and accurate assessment procedure.