Online and offline teaching connection system of college ideological and political education based on deep learning

Education refers to ideologies, traditions, culture, and values that guide education to economics, politics, morals, religions, information, reality, comparative and historical aesthetic, and artistic school knowledge. The challenging characteristics in political education include lack in knowledge sharing, user’s interactive experience, and incentive mechanism has become an essential factor. In this paper, the Deep Learning-Based Innovative Ideological Behavior Education Model (DL-IIBEM ) has been proposed to strengthen the mechanism to promote information exchange, enhance the user’s interactive experience, and make the platform perform efficiently. Knowledge Network Mechanism Analysis is integrated with DL-IIBEM to strengthen user feedback probability, the average probability of completing social media tasks on a popular network, and the predicted utility degree for individual users. The entire platform is dramatically improved. The simulation analysis is performed based on the performance ratio based on data set 1 (98.2%) and 2 (95.3%), skill development ratio (95.3%), accuracy ratio, the teaching methods in ideological and political education, and Students Achievements ratio (98.2%) prove the proposed framework’s reliability.

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Teaching Quality Analysis of College Ideological and Political Education Based on Deep Learning

Journal of Interconnection Networks ◽

10.1142/s021926592147006x ◽

2022 ◽

Author(s):

Liting Feng ◽

Yulong Dong

Keyword(s):

Deep Learning ◽

Simulation Analysis ◽

Retention Rate ◽

Social Activity ◽

Knowledge Retention ◽

Teaching Quality ◽

Ideological And Political Education ◽

Political Education ◽

Quality Analysis ◽

The Impact

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.

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Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

Scientific Programming ◽

10.1155/2021/2348494 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Ke Xu

Keyword(s):

College Students ◽

Deep Learning ◽

Classification Model ◽

Ideological And Political Education ◽

Political Education ◽

Fresh Blood ◽

Starting Point ◽

Evaluation Module ◽

The Subject ◽

Education Model

The teaching of ideological and political theory courses and daily ideological and political education are two important parts of education for college students. With the iterative update of information technology, the individualized development of students, and the reform and innovation of ideological and political education, higher goals and requirements have been put forward for ideological and political education. Some universities have developed new paths in the teaching model, but they have not considered the evaluation module and paid little attention to their own development. They only paid attention to the fact that it injected fresh blood into the reform of education model and ideological education but ignored the improvement of their own quality. Therefore, with these limitations, the learning effect is not satisfactory. Keeping in view these issues, this article defines the concept of deep learning and ideological and political education of college students as the starting point and then analyzes the new precise and personalized concepts, new forms of intelligent teaching and evaluation, and new models of intelligent learning that deep learning brings to college students’ ideological and political education. This is a new path of intelligent linkage with the subject, object, and mediator. It can deepen the reform of the education and teaching mode of individualization, accuracy, interactivity, and vividness of college students’ ideological and political education and improve the evaluation and management of college students’ ideological and political education. The experimental results of the study showed the effectiveness of the proposed study.

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