Research on Personalized Course Recommendation System for Civic Education Based on Adaptive Learning Algorithm

As a new normal under the trend of "Internet+Education", adaptive learning mode is facing the problems of unsuitable resource recommendation and insignificant learning effect while widely popularized.This paper analyzes the learning process of adaptive learning algorithm, makes clear the important components of adaptive learning system centered on learners.Considering the differences in learning styles of different students, the learning style model and learning resource model are constructed successively.By calculating the similarity between students' learning styles and learning resources' learning styles, personalized recommendation based on learning styles is completed.Then we elaborate three personalized resource recommendation algorithms based on learning style filtering recommendation algorithm, collaborative filtering recommendation algorithm, and association rule recommendation algorithm, which adapt to different learning styles in order to recommend resources.Subsequently, the overall framework of the system is designed to form a personalized recommendation system for Civic and Political Education, which consists of three layers: data layer, business layer and user layer.The learning style model is utilized to classify learning styles into four types, namely active and reflective, perceptual and intuitive, visual and verbal, and sequential and global, based on the individual situation of the research subjects.On this basis, the click rate of text resources and video resources recommended by the personalized recommendation system for political thinking education are both above 90.00%and up to 98.71%.It shows that the personalized recommendation system designed in this paper can accurately adapt to the learning preferences and learning styles of the learners, so as to recommend the most compatible learning resources.