Research on Intelligent Recommendation of Teaching Resources for Civics and Political Science Courses for Smart Education Platforms

Abstract

This paper integrates memory-based and SVD-based collaborative filtering algorithms to realize the recommendation of Civic and Political education resources. With the methods of case study and data analysis, a sample study on the construction of a Civic and Political Smart Classroom in WL College is launched to explore the use of intelligent recommendations to obtain Civic and Political education resources in colleges and universities. From the perspective of learning analysis, combined with the social network analysis method, the learning data and interaction of students are analyzed. Based on the feedback from the analysis results, it is convenient for teachers to intervene in students’ learning behavior at the right time. The method in this paper has a recall and accuracy rate of above 95%, which is of good application value, as shown in the results. More than 70% of students and teachers think it can meet their daily personalized learning needs. In terms of independent learning, 43 students exceeded 10 points, accounting for more than 80%. Most students are able to actively participate in classroom interactions as well as online learning discussions, although some of them may not be highly motivated. Therefore, teachers need to understand and personalize instruction for the different situations of their students.

Keywords

• collaborative filtering algorithm
• educational resource recommendation
• interaction situation
• personalization
• 03b70