• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.7-11
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• 2021

This study designs block-based machine learning education program using sound data that can be easily applied in elementary schools. The education program designed its goals and directions based on the results of a demand analysis conducted on 70 elementary school teachers in advance according to the ADDIE model. Scratch in Machine Learning for Kids was used for block-based programming, and the education program was designed to discover regularity of data values using sound data, learn the principles of artificial intelligence, and improve computational thinking in the programming process. In a later study, the education program needs to verify what changes there are in attitudes and computational thinking about artificial intelligence.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.751-759
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• 2021

This study developed an artificial intelligence education program using localized public data as an educational method for improving computing thinking skills of elementary school students. According to the ADDIE model, the program design was carried out based on the results of pre-requisite analysis for elementary school students, and textbooks and education programs were developed. Based on localized public data, the training program was constructed to learn the principles of artificial intelligence using machine learning for kids and scratches and to solve problems and improve computational thinking through abstracting public data for purpose. It is necessary to put this training program into the field through further research and verify the change in students' computational thinking as a result.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.1-6
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• 2021

This study designed an artificial intelligence education program using localized public data as an educational method for improving computational thinking in elementary school students. Program design and development was carried out based on the results of pre-requisite analysis on elementary school students according to the ADDIE model. Based on localized public data, the program was organized to learn the principles of artificial intelligence by utilizing "Machine Learning for Kids" and "Scratch" and to solve problems and improve computational thinking skills through abstracting public data for purpose.Through subsequent research, it is necessary to put this education program into the field and verify the change in students' computational thinking as a result.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.367-375
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• 2021

This study developed and applied an artificial intelligence education program as an educational method for increasing computational thinking of elementary school students and verified its effectiveness. The educational program was designed based on the results of a demand analysis conducted using Google survey of 100 elementary school teachers in advance according to the ADDIE(Analysis-Design-Development-Implementation-Evaluation) model. Among Machine Learning for Kids, we use scratch for block-based programming and develop and apply textbooks to improve computational thinking in the programming process of learning the principles of artificial intelligence and solving problems directly by utilizing numerical data. The degree of change in computational thinking was analyzed through pre- and post-test results using beaver challenge, and the analysis showed that this study had a positive impact on improving computational thinking of elementary school students.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.719-722
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• 2021

최근 인공지능의 새로운 기술들이 하루가 다르게 발전하고 있다. 이에 본 연구에서는 인공지능 교육과 글쓰기 교육을 연계하여 초등학생들의 어휘력 향상을 위한 수업을 설계하고자 한다. 그 방법으로는 본 수업에 앞서 어휘 10문제를 테스트하여 실험에 참가하기 전의 어휘력을 점검한다. 그 후 머신러닝 포키즈를 이용하여 여러 감정에 해당되는 단어들을 다양하게 훈련하도록 하였고, 그 후 관련된 어휘 10문제를 다시 테스트 하였다. 실험 결과 실험에 참가하기 전에는 100점 만점에 58.8점으로 나왔으나 훈련 후의 결과는 평균 68점으로 모든 학생의 성적이 좋아지는 결과를 얻을 수 있었다. 어휘력 문항수가 적은 점과 10명의 실험참가자로 일반화할 수 없는 한계가 있다. 향후 초등교재 한권을 선정하여 어휘를 모두 분석한 후 가장 많이 등장하는 어휘를 골라내어 테스트하여 좀 더 통계적으로 의미 있는 분석을 하고자 한다.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.327-335
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• 2021

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.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.705-712
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• 2021

This study aims to develop artificial intelligence education programs that can be easily applied in elementary schools according to the trend of the times called artificial intelligence. The training program designed the purpose and direction based on the analysis results of the needs of 70 elementary school teachers according to the steps of the ADDIE model. According to the survey, elementary school students developed a machine learning education program to set sound data as the theme of the most accessible in their daily lives and to learn the principles of artificial intelligence in solving problems using sound data in real life. These days, when the need for artificial intelligence education emerges, elementary machine learning education programs that solve daily life problems based on sound data developed in this study will lay the foundation for elementary artificial intelligence education.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.199-208
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• 2021

In this paper, the effect of artificial intelligence convergence education program that provides STEAM education using machine learning platform on elementary school students' STEAM literacy and learning flow was analyzed. A homogeneous group of 44 elementary school 6th graders was divided into an experimental group and a control group. The control group received 10 lessons of general subject convergence class, and the experimental group received 10 lessons of STEAM-based artificial intelligence convergence education using Machine learning for Kids. To develop the artificial intelligence convergence education program, the goals, achievement standards, and content elements of the 2015 revised curriculum to select subjects and class contents is analyzed. As a result of the STEAM literacy test and the learning flow test, there was a significant difference between the experimental group and the control group. In particular, it can be confirmed that the coding environment in which the artificial intelligence function is expanded has a positive effect on learners' learning flow and STEAM literacy. Among the sub-elements of convergence talent literacy, significant differences were found in the areas of personal competence such as convergence and creativity. Among the sub-elements of learning flow, significant differences were found in the areas such as harmony of challenge and ability, clear goals, focus on tasks, and self-purposed experiences. If further expanded research is conducted in the future, it will be a basic research for more effective education for the future.