School of Marxism, Northeast Forestry University, Harbin, Heilongjiang 150040, China.
J Environ Public Health. 2022 Sep 13;2022:2394668. doi: 10.1155/2022/2394668. eCollection 2022.
Ideological and political education (IPE) is aimed at achieving people's free and all-around growth through the use of appropriate methods, and the use of educational methods is integral to the execution of education. Under the influence of big data, it is imperative to strengthen the research on the accuracy of ideology education in colleges and universities (IPECU), which necessitates that ideology educators adopt big data thinking, investigate novel pedagogical approaches, and consistently develop new IPECU conditions. In this paper, a collaborative filtering- (CF-) based algorithm for IPE resource recommendations is presented. Users are given recommendations for educational resources based on their browsing history, browsing patterns, and preferences. The accurate recommendation system can determine users' needs by examining how they use the website in order to suggest more useful information to them. In comparison to the conventional algorithm, the accuracy of the ideological and political education precision recommendation model in this study is 16.75% greater. Teachers can use big data technology to gather students' data information that is dispersed throughout cyberspace, understand students' states in real time, and deliver accurate instructional materials in accordance with students' various states and needs by utilizing the intelligent ideology mode.
思想政治教育（IPE）旨在通过运用适当的方法实现人们的自由全面发展，而教育方法的运用是实施教育的重要组成部分。在大数据的影响下，加强高校思想政治教育精准度（IPECU）的研究势在必行，这需要思想政治教育者采用大数据思维，探索新颖的教学方法，不断发展新的 IPECU 条件。本文提出了一种基于协同过滤（CF）的 IPE 资源推荐算法。根据用户的浏览历史、浏览模式和偏好，为用户推荐教育资源。准确的推荐系统可以通过检查用户如何使用网站来确定用户的需求，从而向他们推荐更有用的信息。与传统算法相比，本研究中的思想政治教育精准推荐模型的准确率提高了 16.75%。教师可以利用大数据技术，收集学生在网络空间中分散的各种数据信息，实时了解学生的状态，并根据学生的各种状态和需求，利用智能思想模式，为学生提供精准的教学资料。
