Proportion of College Students Internet Education Data Based on Big Data Analysis Technology

Title

Proportion of College Students Internet Education Data Based on Big Data Analysis Technology

Subject

Big data
Digital storage
Surveys
Data handling
Trees (mathematics)
Students
Engineering education
Information analysis

Description

In recent years, the rapid development of information science and technology is also changing the way of life of human beings. Especially with the application of advanced technologies such as big data analysis technology, digital media technology and network technology to the field of education, not only is the form of education reformed, college students as one of the subjects of education have also been affected by many definitions. This article aims to study the proportion of college students Internet education (IE) data based on big data analysis technology. Based on data analysis technology, this article sorts out the general situation of college students IE, and summarizes the content and form of college students IE. This paper analyzes the problems and causes of IE for college students through a questionnaire survey, and discusses strategies for the active development of IE for college students from schools, teachers and college students themselves. Survey data shows that among the 508 college students, 86.02% of the students use Tencent Meeting
72.44% of the students use Superstar Learning Link
59.84% of the students use Wisdom Tree
31.89% of the students use MOOC
34.65% of the students use Nail nail. This shows that college students can actively use the Internet platform for learning and play the positive role of the Internet. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
475-483
102

Creator

Yang, Yuxin
Cui, Yong

Date

2022

Type

conferencePaper

Identifier

23674512
10.1007/978-981-16-7466-2_53

Citation

Yang, Yuxin and Cui, Yong, “Proportion of College Students Internet Education Data Based on Big Data Analysis Technology,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/29349.

Output Formats