Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Analysis of Effects of Convergence Education Program about State Classification of the Matters using Machine Learning for Pre-service Teachers

예비교사를 위한 머신러닝 활용 물질의 상태 분류에 대한 융합교육 프로그램의 효과 분석

  • Yi, Soyul (Dept. of Computer Education, Korea National University of Education) ;
  • Lee, YoungJun (Dept. of Computer Education, Korea National University of Education) ;
  • Paik, Sung-Hey (Dept. of Chemistry Education, Korea National University of Education)
  • 이소율 (한국교원대학교 컴퓨터교육과) ;
  • 이영준 (한국교원대학교 컴퓨터교육과) ;
  • 백성혜 (한국교원대학교 화학교육과)
  • Received : 2022.03.28
  • Accepted : 2022.05.20
  • Published : 2022.05.28
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Abstract

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

본 연구는 예비교사의 미래 교육을 위한 인공지능 융합교육 역량을 함양하고, 동시에 학생의 학습 과정에 대한 이해를 증진할 수 있는 교육 프로그램을 개발하고 효과를 분석하는 것을 목적으로 하였다. 이를 위해 물질의 상태 분류를 주제로 머신러닝포키즈와 스크래치3를 활용한 인공지능 융합교육 프로그램을 15주차 분량으로 개발하였다. 개발된 내용은 자발적으로 참여한 K대학교 예비교사들에게 처치되었다. 그 결과, 예비교사들은 머신러닝의 학습을 이해하는 과정을 통해 학생의 학습 과정을 비유적으로 이해할 수 있었다. 또한, 인공지능 교수효능감의 사전-사후 t검정 결과는 t=-7.137(p< .000)으로 통계적으로 유의한 향상을 보였다. 따라서 본 연구에서 개발한 인공지능 융합교육 프로그램은 교생실습 외에 비간접적인 방식으로 예비교사의 학생에 대한 이해를 높일 수 있는데 도움이 되고, 인공지능 교육 역량 함양에 기여할 수 있음이 시사된다.

Keywords

Acknowledgement

This research was supported by the Ministry of Education and the National Research Foundation of Korea(NRF) (NRF-2019S1A5C2A04081191)

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