The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

Analysis of the teaching quality on deep learning-based innovative ideological political education platform

The contributions of AI in the development of ideological and political perspectives in education

At present, low teaching efficiency has been the common problem of ideological and political education in colleges and universities in China. It is essential to improve the teaching efficiency and realize the intelligent information transformation of the ideological and political courses in colleges and universities. First, the relationship between ideological and political courses and the educational psychology of college students was analyzed based on the theoretical characteristics of educational psychology and college ideological and political courses. Additionally, the teaching efficiency of ideological and political courses based on deep learning (DL) was analyzed through a literature survey. Combined with online teaching modes such as the flipped classroom and Massive Open Online Courses, a comprehensive online teaching mode of college ideological and political courses was proposed via educational psychology and the Single Shot MutiBox Detector networks of DL. Then, a total of 100 research subjects were selected randomly from the freshmen and sophomores of the Southwest University of Science and Technology, and their acceptability to the online ideological and political courses was analyzed by a questionnaire survey. The results show that the adopted questionnaire had high reliability and validity, and the proportion of respondents of different genders, grades, and majors was essentially balanced. More than half of the students had a good understanding of the comprehensive ideological and political courses and made progress in their values, ideology, morals, and knowledge reserves. More than half of the students had a positive attitude to the course, and they thought that the class atmosphere of the course was active, which was conducive to a satisfactory learning effect. This indicates that the teaching strategy of ideological and political courses in colleges and universities that integrates educational psychology, DL, and online information can attract students. The contribution of this study is that the research outcome can be applied to the concrete formulation of the teaching strategies of ideological and political courses for college students.

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Deep Learning-Based Teaching Strategies of Ideological and Political Courses Under the Background of Educational Psychology.

Intelligent education evaluation mechanism on ideology and politics with 5G: PSO-driven edge computing approach

With the development of modern mobile communication, artificial intelligence (AI) has begun to enter people’s life, and it is also constantly changing the modern education mode. First, the structure of BPN (back-propagation network) is designed in this paper. On this basis, genetic algorithm is applied to optimize, which can accelerate the convergence speed and achieve the effect of global optimization. The results obtained from the research also meet the expected value, realizing the purpose of optimizing the BP algorithm. This paper analyzes the AI teaching expert system, summarizes their functions and characteristics, and points out that the college students’ ideological and political teaching system based on the mobile AI terminal can be used as the teaching manager, teaching assistant, and even as the teaching object to guide students’ learning. At the same time, the article also points out that the application and development direction of artificial intelligence in the teaching field can be divided into three stages: primary application, intermediate application, and advanced application, so as to provide theoretical guidance for the construction and analysis of ideological and political teaching system for college students using mobile artificial intelligence terminals.

Analysis on the construction of ideological and political education system for college students based on mobile artificial intelligence terminal

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Analysis on the construction of ideological and political education system for college students based on mobile artificial intelligence terminal