정보교육학회논문지 (Journal of The Korean Association of Information Education)

  • 제25권2호
  • /
  • Pages.327-335
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  • 2021
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  • 1229-3245(pISSN)
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  • 2733-8428(eISSN)
한국정보교육학회 (Korea Association of Information Education)
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데이터 라벨링 중심의 머신러닝 교육이 초등학생 컴퓨팅 사고력에 미치는 효과

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

  • 투고 : 2020.12.03
  • 심사 : 2020.12.28
  • 발행 : 2021.04.30
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초록

본 연구는 초등학생의 컴퓨팅 사고력을 향상시키기 위한 교육 방법으로 데이터 라벨링 중심의 머신러닝 교육 프로그램을 개발하여 적용한 후 그 효과를 검증하였다. 교육 프로그램은 현직 초등학교 교사 100명을 대상으로 실시한 사전 요구분석 결과를 바탕으로 설계 및 개발을 진행하였다. 개발한 교육 프로그램의 효과를 검증하기 위하여 K 초등학교에 재학 중인 6학년 학생 17명을 대상으로 1일 2차시씩 총 6주간 12차시의 교육을 진행하였다. 해당 교육이 컴퓨팅 사고력 향상에 미친 효과를 측정하기 위해 ' 버챌린지(Bebras Challenge)'를 활용하여 사전 사후 검사를 진행하여 교육적 효과를 분석하였다. 분석 결과 데이터 라벨링 중심의 머신러닝 교육이 초등학생의 컴퓨팅 사고력 향상에 기여한 것으로 나타났다.

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.

키워드

과제정보

본 논문은 2021년도 제주대학교 교육·연구 및 학생지도비 지원에 의하여 연구되었음.

참고문헌

  1. Bebras Challenge(2020). Introduction to Bebras Challenge. https://bebraschallenge.org/
  2. ISTE Introduction to ISTE. https://www.iste.org/
  3. 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
  4. 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
  5. 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. https://doi.org/10.14352/jkaie.21.1.137
  6. Kim E.J(2018). Beaver Challenge 2017 Reliability Analysis as a Computing Thinking Assessment Tool : The Korean Group III
  7. Kanako Onishi(2019). Easiest AI Introduction, 1st ED. Seoul: Atio.
  8. Lee. B.B.(2020). Impact of Data Imbalance on Machine Learning Model Performance.
  9. 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.
  10. 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. https://doi.org/10.14352/jkaie.2019.23.2.189
  11. Lee. Y.H.(2020). Artificial Intelligence for Everyone.
  12. Lee. Y.J.(2014). Research for Introducing Computational Thinking into Primary and Secondary Education.
  13. Machine Learning For Kids(2020). Introduction to Machine Learning For Kids.
  14. Margaret Rouse(2019). Definition of Data labeling. https://whatis.techtarget.com/definition/data-labeling
  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. Seo. J.Y.(2019). SW education plan for fostering next-generation SW talents based on AI education.
  18. Wing J.M(2006). Computational Thinking.