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http://dx.doi.org/10.14352/jkaie.2021.25.2.327

Effect of Machine Learning Education Focused on Data Labeling on Computational Thinking of Elementary School Students  

Moon, Woojong (Jeju National University)
Kim, Bomsol (Jeju National University)
Kim, Jungah (Jeju National University)
Kim, Bongchul (Jeju National University)
Seo, Youngho (Jeju National University)
OH, Jeongcheol (Dopyeong Elemantary School)
Kim, Yongmin (Jeju City Office of Education)
Kim, Jonghoon (Jeju National University)
Publication Information
Journal of The Korean Association of Information Education / v.25, no.2, 2021 , pp. 327-335 More about this Journal
Abstract
This study verified the effectiveness of machine learning education programs focused on data labeling as an educational method for improving computational thinking of elementary school students. The education program was designed and developed based on the results of a preliminary demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed education program, 17 sixth-grade students attending K Elementary School were given 2 classes per day for a total of 6 weeks. In order to measure the effect of the training on improving computational thinking, the educational effects were analyzed by conducting pre-post-inspection using the "Beaver Challenge". According to the analysis, machine learning education focused on data labeling contributed to improving computational thinking of elementary school students.
Keywords
Data Labeling; AI; Machine Learning; Machine Learning for Kids; Computational Thinking;
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  • Reference
1 Seo. J.Y.(2019). SW education plan for fostering next-generation SW talents based on AI education.
2 ISTE Introduction to ISTE. https://www.iste.org/
3 Kim. K.S, Park Y K(2017). A Development and Application of the Teaching and Learning Model of Artificial Intelligence Education for Elementary Students. Journal of The Korean Association of information Education, 21(1), 137-147.   DOI
4 Lee. B.B.(2020). Impact of Data Imbalance on Machine Learning Model Performance.
5 Lee. Y.H.(2020). Artificial Intelligence for Everyone.
6 Margaret Rouse(2019). Definition of Data labeling. https://whatis.techtarget.com/definition/data-labeling
7 Bebras Challenge(2020). Introduction to Bebras Challenge. https://bebraschallenge.org/
8 Jang. J.H.(2018). New Artificial Intelligence Technology Self-study Artificial Intelligence. Introduction to SAMSUNG SDS. https://www.samsungsds.com/global/ko/support/insights/Generative-adversarial-network-AI.html
9 Kim. B.S, Moon. W.J.(2020). Development of Machine Learning Education Program Using Data Labeling. Proceeding of the KAIE Summer Conference 2020, 157-163
10 Kim E.J(2018). Beaver Challenge 2017 Reliability Analysis as a Computing Thinking Assessment Tool : The Korean Group III
11 Kanako Onishi(2019). Easiest AI Introduction, 1st ED. Seoul: Atio.
12 Lee. E.K.(2020). A Comparative Analysis of Contents Related to Artificial Intelligence in National and International K-12 Curriculum. J ournal of The Korean Association of information Education, 23(1), 37-44.
13 Lee. Y.J.(2014). Research for Introducing Computational Thinking into Primary and Secondary Education.
14 Machine Learning For Kids(2020). Introduction to Machine Learning For Kids.
15 Oh. P.R. (2020). Introducing 'AI and convergence' in SW education. Introduction to Chosun Edu. http://edu.chosun.com/site/data/html_dir/2020/
16 Park.S.J.(2019). Analysis of Bebra Challenge Results through Algorithm Education.
17 Lee. Y.H.(2019). An Analysis of the Influence of Block-type Programming Language-Based Artificial Intelligence Education on the Learner's Attitude in Artificial Intelligence. Journal of The Korean Association of information Education, 23(2), 189-196.   DOI
18 Wing J.M(2006). Computational Thinking.