スケーラブルなAI教育の実現を目指す完全自動オンライン学習環境で見つかった５つの課題
2019年7月3日
「Review of Integrative Business and Economics Research, Vol. 9, Issue 3」掲載論文（SIBR Best Paper賞受賞）＜共著：瀬谷啓介、岡谷貴之、松尾豊、小林延至、白坂成功＞
【原文タイトル】
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
【日本語要約】
近年、情報技術の急速な発展とともに、高度な技術を持つ技術系人材の不足が世界的な課題となっています。産官学をあげてその効果的な解決策を模索する中、オンライン教育は大人数のトレーニングを実現する一つの重要な手法として注目を集めていますが、人工知能などの先端IT分野でそれを効果的に実現するための知見は限られているのが現状です。本論文では、オンライン教材「機械学習」「ディープラーニング」の提供を通して見出した5つの課題を紹介するとともに、効果的な学習に向けてその解決策について議論しています。
【原文要約】
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.
→論文へのリンクはこちら（原文英語）