Development of a Digilog...(Journal of Computational and Theoretical Nanoscience, 2021.05) > 자유게시판

• 목록

논문명

Development of a Digilog Learning Model for Training on the Principles of Artificial Intelligence Learning in Elementary Education

Abstract

The importance of nurturing human resources who will lead the 4th Industrial Revolution is increasing,

and artificial intelligence is a core factor of innovative technologies. Therefore, developing

various and interesting teaching methods for principles of artificial intelligence is necessary. This

article suggests teaching principles of artificial intelligence by convergence of digital and analogue,

called digilog. Students get to know how machines can learn and operate, which is digital, with

paper worksheets and several physical teaching aids, which are analogue. In digilog way, students

figure out the principles of image recognition. There are two methods, MAX and filtration box. The

principles of artificial intelligence are too abstract to understand for elementary learners who are

yet at concrete operational period, according to Piaget. Therefore, the convergence of digital and

analogue is effective for teaching and learning about artificial intelligence in elementary education.

Elementary learners examine colorful virtual images in their worksheet and use their hands and

pencils to trace artificial intelligence’s work. They end up with figuring out how artificial intelligence

compresses inserted images into smaller reference images step by step. With the offered method

and developing more diverse digilog elements, elementary learners’ knowledge and experiences

necessary for the future society will be increased.

논문 정보

Yuri Hwang1 and Namje Park1 2 ∗

1Department of Software Convergence Education, Jeju National University Graduate School, Jeju,

Jeju Special Self-Governing Province, 63294, Korea

2Department of Computer Education Teachers College, Jeju National University, Jeju-si,

Jeju Special Self-Governing Province, 63294, Korea

Journal of Computational and Theoretical Nanoscience, Vol. 17, 1–4, 2020

doi:10.1166/jctn.2020.9625