The Design of Personalized Learning Resource Recommendation System for Ideological and Political Courses

Abstract

Colleges and universities increasingly incorporate ideological and political (IP) concepts into their courses as a fundamental prerequisite and a rising IP education trend under changing conditions. Students have difficulty sifting through the ever-growing amount of online information to locate what they need in learning resources. Technology-enhanced learning encompasses any technology that helps students study more effectively. This paper suggests a personalized learning resource recommendation system (PLRRS) for IPC. Personal learning recommendation systems (PLRSs) that do their task well will help students cope with the existing information overload. They will make sure that they receive the correct information at the right time and in the right format for their particular needs. E-learning systems that intentionally personalize their courses to the preferences, objectives, skills, and interests of the students they serve are engaging in personalized learning. In the last several years, researchers have been looking at ways to assist instructors in enhancing e-learning. Personalized learning scenarios are created by picking the most relevant learning objects based on an individual’s profile. A test score greatly improved for students in IPC after using the model in this research, which suggests that this model has a strong promotion value.