Big Data Web Crawler Analysis of Online Professional Course Requirements
- DOI
- 10.2991/assehr.k.211011.075How to use a DOI?
- Keywords
- Online professional courses, Web Crawler, Text Analysis
- Abstract
In the context of the era of big data, the development and progress of network technology has brought learners multiple learning methods and strategies of personalized learning choices.Although the development of online courses in our country has begun to take shape, many problems have emerged in the implementation process. There are still obvious obstacles in the design of courses, the use of teaching tools, and the transformation of educational concepts. As for professional courses, due to their own characteristics, the online teaching of professional courses is particularly hard. In view of these problems, this article has done the following work: Use Python to crawl online comments on professional courses, generate text documents and then perform text analysis. In the text analysis, Python jieba library is first used for word segmentation and word frequency statistics, and then Python wordcloud library is used to generate comment wordcloud to study learners’ demands for online professional courses. On the basis of the above analysis, summary and suggestions are given to provide reference for the development of online professional courses in our country, to promote the good development of online professional courses, and to provide convenience for relevant professional learners.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Song Shuo AU - Wu Guangcuan AU - Peng Yiping PY - 2021 DA - 2021/10/12 TI - Big Data Web Crawler Analysis of Online Professional Course Requirements BT - Proceedings of the 2021 6th International Conference on Modern Management and Education Technology（MMET 2021） PB - Atlantis Press SP - 414 EP - 418 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.211011.075 DO - 10.2991/assehr.k.211011.075 ID - Shuo2021 ER -