Identifying Issues for Learners in Completing Online Courses on Machine Learning and Deep Learning: Five Issues Found in a Fully Automated Learning Environment for the Purpose of Scalable AI Education

Keisuke Seya, Takayuki Okatani, Yutaka Matsuo, Nobuyuki Kobayashi, Seiko Shirasaka

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Information technology is becoming increasingly sophisticated and rapidly developing. Although the demand for highly skilled technical human resources is rising, the supply is insufficient. Since this is a global problem, not only industries but also governments have become active in trying to find a solution. Online education is one method which enables training of a large number of people. However, our knowledge on how to train a large number of technical professionals in highly advanced emerging technologies such as artificial intelligence is insufficient. In this paper, we propose a fully automated online teaching method for learners who want to understand Machine Learning and Deep Learning and identify the issues that learners face in completing online courses on their own in a fully automated learning environment. This study concludes by presenting the five issues identified through the data collected from the Deep Learning and Machine Learning online courses designed by the proposed teaching method.

本文言語English
ページ(範囲)35-54
ページ数20
ジャーナルReview of Integrative Business and Economics Research
9
3
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • ビジネス、管理および会計(その他)
  • 経済学、計量経済学および金融学(その他)

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