Development of CNN-Based Data Crawler to Support Learning Block Programming

HuiJae Park, JaMee Kim and WonGyu Lee
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HuiJae Park: Department of Computer Science and Engineering, Graduate School, Korea University, Seoul 136701, Korea
JaMee Kim: Major of Computer Science Education, Graduate School of Education, Korea University, Seoul 136701, Korea
WonGyu Lee: Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 136701, Korea

Mathematics, 2022, vol. 10, issue 13, 1-12

Abstract: Along with the importance of digital literacy, the need for SW(Software) education is steadily emerging. Programming education in public education targets a variety of learners from elementary school to high school. This study was conducted for the purpose of judging the proficiency of low school-age learners in programming education. To achieve the goal, a tool to collect data on the entire programming learning process was developed, and a machine learning model was implemented to judge the proficiency of learners based on the collected data. As a result of determining the proficiency of 20 learners, the model developed through this study showed an average accuracy of approximately 75%. Through the development of programming-related data collection tools and programming proficiency judging models for low school-age learners, this study is meaningful in that it presents basic data for providing learner-tailored feedback.

Keywords: computer education; programming education; leaner classification; log collection; Scratch (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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