Research on the framework of university ideological and political education management system based on artificial intelligence

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Xu Sun, Yu Zhang
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引用次数: 0

Abstract

The importance of the management of ideological and political theory courses in colleges and universities is objective to the importance of ideological and political theory courses. At present, the management of ideological and political theory courses in colleges and universities has big problems in both macro and micro aspects. This paper combines artificial intelligence technology to build an intelligent management system for ideological and political education in colleges and universities based on artificial intelligence, and conducts classroom supervision through intelligent recognition of student status. The KNN outlier detection algorithm based on KD-Tree is proposed to extract the state information of class students. Through data simulation, it can be known that the KD-KNN outlier detection algorithm proposed in this paper significantly improves the efficiency of the algorithm while ensuring the accuracy of the KNN algorithm classification. Through experimental research, it can be seen that the construction of this system not only clarifies the direction of management from a macro perspective, but also reveals specific methods of management from a micro perspective, and to a certain extent effectively solves the problems in the management of ideological and political theory courses in colleges and universities.
基于人工智能的高校思想政治教育管理系统框架研究
高校思想政治理论课管理的重要性是对思想政治理论课重要性的客观反映。当前,高校思想政治理论课管理在宏观和微观两个方面都存在较大问题。本文结合人工智能技术,构建基于人工智能的高校思想政治教育智能管理系统,通过智能识别学生学籍进行课堂监督。提出了基于KD-Tree的KNN离群点检测算法,提取班级学生的状态信息。通过数据仿真可知,本文提出的KD-KNN离群点检测算法在保证KNN算法分类准确性的同时,显著提高了算法的效率。通过实验研究可以看出,该体系的构建既从宏观上明确了管理方向,又从微观上揭示了具体的管理方法,在一定程度上有效解决了高校思想政治理论课管理中存在的问题。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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