Case Study and Practice of Ideological and Political Education in the Course "Wireless Sensor Network Technology and Applications"

In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.

Research on the Reform and Development of Ideological and Political Theory Classes for College Students in the Context of Practical Parenting

Wang

2024

Research on Sentiment Analysis Techniques for Online Ideological and Political Education

Zhou,

Yang,

Zheng

et al. 2024

Network ideological and political education is a great extension of school education, but the complex and confusing network information is also easy to give students bad emotional guidance. In this paper, microblogging data and topic clustering technology are used to obtain the emotional text data of network ideological and political education, based on the LDA model for topic feature extraction, to remove the data that has a lower degree of relevance to ideological and political education emotion in the data. Then, combined with the Jieba segmentation tool, based on the TF-IDF algorithm, for keyword extraction of the data that has been segmented. The text semantic features extracted based on the Word2Vec word vector model are used as the input information of this model, and the unique memory unit and gating structure of LSTM are utilized to solve the problem of storing memory of a priori knowledge and the problem of gradient dissipation caused by too long time threshold in long text classification. The Word2Vec-LSTM text sentiment classification model is developed and trained using Python and related machine learning libraries, and comparative experiments are carried out to validate the algorithm’s effectiveness and feasibility. The performance of Word2Vec-LSTM sentiment analysis on the dataset is higher than Word2Vec and LSTM algorithms. In addition, the value of sentiment perplexity is the smallest when the number of topics is set to be about 8, and its value is 0.542, and the positive sentiment density reaches the maximum value (0.648) as the sentiment score grows to 0.48. This paper explores the application of emotion analysis results in network ideological and political education to further improve the construction of a network ideological and political education system.

Research on the Application of SVM in Ideology Analysis and the Enhancement of Ideological and Political Education Effectiveness

Huang,

Shen,

Wang

2024

The consciousness and behavior of students in the Ideological and Political Education curriculum generate massive amounts of data information, how to allow educators to quickly obtain information from the massive amount of text data is very important to improve the energy efficiency of classroom education. SVM technology is employed in this paper for data mining and text classification. Firstly, using the web crawler method, the text of messages related to the Ideological and Political Science course posted by students on social networks is collected. The short text was preprocessed before performing sentiment analysis, which mainly included interaction information filtering, word segmentation, and lexical labeling. Then, the SVM model suitable for students’ ideology analysis is constructed, using the Gauss radial basis kernel function to accurately depict the distribution structure of the data, and the L1-SVM model with more stable computational performance is also proposed. The extension method of the classification algorithm in the real number domain is summarized at the end. This algorithm’s accuracy is 78%, and its F1 value is 80%, which is higher than the other three algorithms. DAG-SVM and recall are both optimized to a lesser extent. Overall, the classification efficiency of the algorithm in this paper has been improved. The positive effect of this paper’s algorithm on improving the effectiveness of Ideological and Political Education can be seen in the significant increase in the learning interest of the experimental class.