Abstract

With the development of big data and data mining technology, machine learning has been applied in many fields. However, there are a large number of difficulties for students who majored in ideological and political education. It is very necessary for those students to integrate machine learning technology into ideological and political education courses. In this paper, we introduced how to integrate machine learning into ideological and political education courses in class. Firstly, we explained what teachers should do before/in/after class for teaching machine learning courses and what students should prepare. Secondly, we took the introduction section of machine learning courses as an example to connect each content with ideological and political education and illustrate them in the way of ideological and political education. Thirdly, we took the decision tree algorithm that belongs to machine learning as an example to explore the ideological and political education philosophy in the decision tree algorithm. Finally, we make a questionnaire from the perspective of learning attitude, learning influence, and learning effect to investigate the outcomes of students with our teaching way. Our results presented valuable meaningful information for students who majored in not only computer science but also ideological and political education, thus promoting the progress of interdisciplinary and making machine learning courses understood more easily in the class of ideological and political education.

Highlights

  • Big data technology refers to a technical system including big data collection, preprocessing, storage, analysis, and visualization

  • Cloud computing is a core principle of big data analysis and processing technology, and it is the basic platform and supporting technology for big data analysis and application. e most typical cloud computing is the big data processing technology represented by distributed file system GFS, batch processing technology MapReduce, distributed database Big Table, and the open-source data processing platform Hadoop generated on this basis [3]. e key technology of big data mainly includes the following: (1)

  • The introduction of machine learning into the teaching of ideological and political courses has improved the enthusiasm learning and promoted the mastery of ideological and political courses and machine learning courses. erefore, this teaching method has a positive impact on improving students’ course learning outcomes

Read more

Summary

Introduction

Big data has the characteristics of various types, huge capacity, fast processing speed, and low-value density [1]. Erefore, in the era of big data, a comprehensive study of the application of machine learning in data mining has promoted the sustainable development of China’s information society. Big data technology refers to a technical system including big data collection, preprocessing, storage, analysis, and visualization. It can be roughly divided into six categories: big data collection, storage, management, big data analysis and mining, big data interpretation, and application. Cloud computing is a core principle of big data analysis and processing technology, and it is the basic platform and supporting technology for big data analysis and application. E most typical cloud computing is the big data processing technology represented by distributed file system GFS, batch processing technology MapReduce, distributed database Big Table, and the open-source data processing platform Hadoop generated on this basis [3]. Cloud computing is a core principle of big data analysis and processing technology, and it is the basic platform and supporting technology for big data analysis and application. e most typical cloud computing is the big data processing technology represented by distributed file system GFS, batch processing technology MapReduce, distributed database Big Table, and the open-source data processing platform Hadoop generated on this basis [3]. e key technology of big data mainly includes the following: (1)

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call