• Journal of Creative Information Culture
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• v.7 no.4
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• pp.265-277
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• 2021

Data is the core of AI technology. With the development of technology, AI technology is also accelerating as the amount of data increases explosively than before. However, compared to the interest in AI education, research on data education with AI is still insufficient. According to the case analysis of exsisting AI data education, there were cases of educating the process and part of data science, but it was hard to find studies related to data collection. Cause physical computing tools have a positive effect on AI education for elementary school students, data collection cases using tools were studied, but researches related to data collection were rare. Therefore, in this study, an efficient data collection method using physical tools was designed. A structural diagram of a data collection program was created using COBL S, a modular physical computing teaching tool, and examples of program screens from the service side and the user side were configured. This study has limitations in that the establishment of an AI education platform that can be used in conjunction with future program production and programs should be prioritized as a proposal in terms of design.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.779-790
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• 2021

This study analyzed the effect of AI maker coding education on improving students' computational thinking. The subjects of the study were 10 students at H Elementary School in Ansan, and a total of 8 AI maker coding education using the Instructional Model for Maker Education based on SW Coding was applied to students to find out the degree of improvement of computational thinking. Students who participated in the class performed a process of solving real-life problems through coding and making activities, measured the degree of improvement in computing thinking before and after education through a computing thinking test paper, and observed students' thinking processes related to computing thinking components through interviews. As a result, it was confirmed that the average score of all students' computational thinking skills was improved, and the deviation of scores between students decreased. Through the interview, it was found that students actively utilize their thinking skills related to computational thinking skills in the problem-solving process. Through this, it was confirmed that AI maker coding education can have a positive effect on improving students' computing thinking skills.