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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)35-54
Number of pages20
JournalReview of Integrative Business and Economics Research
Volume9
Issue number3
Publication statusPublished - 2020

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
  • Online Course

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)

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