• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.551-562
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• 2020

The purpose of this study is to investigate the effects of Data Science Education classes using PBL-based App Inventor on elementary student Computational Thinking and Creativity. Based on the results of the pre-requisite analysis by Rossett's demand analysis model, PBL-based Data Science Education class was designed according to the procedure of ADDIE model which is 42 hours of classroom instruction for elementary student. As a result of the Paired t-test, it was proved that the Computational Thinking was statistically significantly improved in the post-test. In addition, as a result of the Paired t-test and Wilcoxon's signed rank test, it was found that the sub-factors of Creativity were 'Originality', 'Fluency', 'Closure', 'Average', and 'Index'. Therefore, it was confirmed that the PBL-based Data Science Education class using App Inventor is effective in improving Computational Thinking and Creativity of elementary student.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.189-196
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• 2023

This paper analyzes the level of satisfaction of two groups of teachers who were educated about artificial intelligence using App Inventor. The participants were 13 pre-service and 9 in-service elementary school teachers and the data was collected using a questionnaire. As a result of the study, in-service teachers were all more satisfied than pre-service teachers in terms of interest, difficulty, and participation in the education. In addition, the questions investigating whether education helped motivate learning of artificial intelligence and whether there is a willingness to apply it to elementary classes in the future were also more positive for in-service teachers than for pre-service teachers. In general, pre-service teachers had somewhat more negative views than in-service teachers, but they were more positive than in-service teachers in terms of whether the education helped improve their understanding of artificial intelligence and whether they were willing to participate in additional education. Analysis of the Mann-Whitney test to see if there was a significant difference in satisfaction between the two groups showed no significance. This may be because most of the students in the two groups already had block-type or text-type programming experience, so they were able to participate in the education without any special resistance or difficulty with App Inventor, resulting in high levels of satisfaction from both groups. The results of this study can provide basic data for the future development and operation of programs for artificial intelligence education for both pre-service and in-service elementary school teachers.