Improving online ideological and political precision education in universities based on knowledge mapping

Abstract

Empowering precision innovation through big data to improve the effectiveness of ideological and political education in colleges and universities can effectively promote cultivating the new generation of the times. Based on learners’ learning behaviors and quantified learning behavior indicators, this paper proposes a three-branch division model based on a cognitive level to reduce the “Matthew effect” of collaborative filtering algorithms in path recommendation and improve the cognitive level of online learners. Based on the three-branch decision theory, we select the decision with the highest expected utility as the final decision and recommend the learning content suitable for the learners’ development. The analysis of the functional requirements of the personalized learning recommendation system based on knowledge mapping, the overall structure diagram of the system, and the construction process of each component module are proposed. By analyzing the influence of ideological and political education on college students, about 80% of college students generally think that ideological and political education has improved political character, strengthened ideals and beliefs, enhanced national sentiment, standardized speech and behavior, improved comprehensive literacy, and enriched after-school knowledge. More than 80% of students, teachers, and leaders are satisfied with the personalized recommendation model proposed in this paper, and less than 10% are dissatisfied. Therefore, big data technology can help improve students’ Civics learning effect and has strong applicability.

Keywords

• big data technology
• collaborative filtering algorithm
• knowledge graph
• three-branch decision making
• 97b20