Survey and Analysis of the MOOCs Platform at AI Industry Talent Training Courses

DOI

10.2991/assehr.k.200727.114How to use a DOI?

Keywords

Talent training, Artificial intelligence, Human-machine interaction, Online courses, MOOCs

Abstract

The massively open online course (MOOCs) teaching platform has risen rapidly in recent years, using the ubiquitous Internet to break geographical and time constraints, and making it easier for people to acquire knowledge. The training of AI talent has always been the focus of the development and competitiveness of various countries. Therefore, the research and discussion of Udacity, Coursera, edX, Xuetang Online, Chinese University MOOC, and Good University Online, including six well-known domestic and foreign MOOCs platforms, have been conducted. Related course areas. The study found that: (1) the current AI curriculum area covers 25 subject areas, and data science and computer science is particularly prominent; (2) except Udacity, the number of AI courses on the remaining five platforms is relatively small; (3) AI courses Many well-known institutions and enterprises have cooperated to set up the courses; (4) The courses on foreign platforms are around four to eight weeks, and the domestic platforms transfer the semester courses to the platforms, and the courses are up to 26 weeks (one academic year). The results of this study can provide a reference for universities or industries engaged in AI talent training.

Copyright

© 2020, 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/).

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TY  - CONF
AU  - Fang Yu-Shen
AU  - Luo Ke-Yi
AU  - Huang Ying-Yan
AU  - Huang Wei-Sheng
AU  - Li Feng-Ping
PY  - 2020
DA  - 2020/07/27
TI  - Survey and Analysis of the MOOCs Platform at AI Industry Talent Training Courses
BT  - Proceedings of the 2020 5th International Conference on Humanities Science and Society Development (ICHSSD 2020)
PB  - Atlantis Press
SP  - 287
EP  - 291
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200727.114
DO  - 10.2991/assehr.k.200727.114
ID  - Yu-Shen2020
ER  -