• Journal of The Korean Association of Information Education
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• v.23 no.3
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• pp.197-206
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• 2019

In this study, we analyzed the effects of data visualization education with using Python on the improvement of computing thinking ability of the 6th grade students of elementary school. Based on the results of the needs analysis of 60 elementary school teachers and 120 elementary school students, we developed the data visualization education program. In the developed educational program, 24 elementary school students were trained for 6 days and 36 hours in total. Thereafter, students were subjected to pre- and post-comparison tests. As a result of the analysis, it was found that the data visualization education with using Python is effective in improving the Computational cognition, Fluency, Originality, Elaboration of the 6th grade students in elementary school.

• Journal of The Korean Association of Information Education
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• v.18 no.1
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• pp.25-33
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• 2014

Due to the recent development of educational robot hardwares, processing speed and scalability have been greatly improved. Thus, the robot hardwares that are compatible with temperature sensor for MBL and gyro sensor made a data logging possible. Students can conduct an experiment on scientific research and prediction, collecting and data analysis with robots that can process data logging. Therefore this research constructed and adopted science project class that introduced a Smart r-Learning that utilizes Class SNS and smartphone. As a result of applying a data logging smart r-Learning to elementary school 5th graders, it has shown that the students' logical thinking ability four of the six areas have been improved in t-test.

• Journal of Creative Information Culture
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• v.6 no.2
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• pp.65-75
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• 2020

The necessaries of Flowchart learning for software education have been discussed but most studies were conducted on learning methods. In this study, Unplugged Flowchart Learning programs for fifth grade students were developed and taught, and their effectiveness were analyzed. The programs were made of 8 themes(16 periods) based on the learner's levels. The effectiveness of the programs were qualitatively analyzed based on classwork sheets, as well as observation and interview. Computational Thinking tests were pre-tested and post-tested for qualitative analyses. This study found that all sub-areas of CT of the students who took the Unplugged flowchart learning program were significantly improved as well as the overall scores of CT. In particular, students' improvements in the area of abstraction and automation was notable. Various interactions between teacher-learners and learners-learners were observed during class, and were found to have positive effects on changes in learners' attitudes and perceptions.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.51-58
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• 2016

The importance of SW education is being stressed recent days, and the App Inventor is getting attention as a tool of SW education. In this study, I have developed an app Inventor instruction model, which is based on the Design Based Learning and integrated with elements of computational thinking. And I taught the student to apply this model. and then analyzed the app production process and the changes of student. In developing the app, students defined the problem and made a plan to resolve them. And this student had have a sense of accomplishment and self-confidence through practical experience to implement it in their own source code.

• Journal of The Korean Association of Information Education
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• v.22 no.6
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• pp.689-699
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• 2018

The purpose of this study was to investigate the prediction of self-regulated learning, GRIT and parents support on computational thinking and learning satisfaction in online software education. The participants were 71 elementary students who attended to an online software education which K university offered in Spring 2018. The 63 of cases were used to analyze by SPSS. The key findings were as follows: First, self-regulated learning and GRIT significantly predicted computational thinking. Second, self-regulated learning and GRIT significantly predicted learning satisfaction. This research suggested the implications for computational thinking and learning satisfaction in online software education.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.1-14
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• 2020

This study investigated the effects of SW-efficacy on computational thinking STEAM literacy and the dual mediation effects of interest and student engagement in the relationship between SW-efficacy, computational thinking and STEAM literacy in robot-utilized SW convergence education. For achieving this purpose, robot-utilized SW convergence education targeting 146 middle schools students were carried out, post-survey regarding SW-efficacy, computational thinking, STEAM literacy, interest, and learning engagement were conducted. Collected data were analyzed by SPSS macro process for analysis of the dual mediation effect. Results were as follows: first, SW-efficacy had a significant effect on computational thinking and STEAM literacy. Second, interest did not mediate the relationship between SW efficacy, computational thinking and STEAM literacy, while student engagement mediated it. Third, SW efficacy had a significant effect on computational thinking and STEAM literacy dual-mediating effect of interest and student engagement. These results imply the roles of SW efficacy, interest and student engagement to improve computational thinking and STEAM literacy in robot-utilized SW education and provide implications with regard to robot-utilized SW education and instructional directions.

• Communications of Mathematical Education
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• v.35 no.2
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• pp.153-172
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• 2021

Various competencies such as critical thinking, systems thinking, problem solving competence, communication skill, and data literacy are likely to be required in the 4th industrial revolution. The competency regarding data literacy is one of those competencies. To nurture citizens who will live in the future, it is timely to consider research on teacher education for supporting teachers' development of statistical thinking as well as statistical knowledge. Therefore, in this study we developed and implemented a data analysis project for pre-service teachers to understand their changes in statistical knowledge in addition to their experiences of data-driven decision making process that required them utilizing their statistical thinking. We used a mixed method (i.e., sequential explanatory design) research to analyze the quantitative and qualitative data collected. The findings indicated that pre-service teachers have low knowledge level of their understanding on the relationship between population means and sample means, and estimation of the population mean and its interpretation. When it comes to the data-driven decision making process, we found that the pre-service teachers' experiences varied even when they worked as a small group for the project. We end this paper by presenting implications of the study for the fields of teacher education and statistics education.

• The Mathematical Education
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• v.63 no.2
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• pp.209-231
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• 2024

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.131-139
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• 2019

From 2019, software education has become a required subject for all elementary school students. However, many teachers are still unfamiliar with how the classes should be instructed. So this paper presented the meaning, detailed competencies and achievement standard in order to help in the collection, analysis and representation of data among the computational thinking that are key to software education. And it also suggested the applicability of the classes. The full course of the paper is summarized as follows. First, existing studies have summarized the meaning, detail and achievement standard of data related competencies. Based on this, a preliminary investigation was instructed. Pilot study carried out both FGI and closed questions at the same time. This was done in response to the survey's questionnaire reflecting the opinions of experts. Second, the results of the questionnaire generated as a result of the above were verified for validity, stability, and reliability among the PhD, PhD courses, software education teachers, and software education workers. Third, I developed and applied the five lessons as a class objective as 'Choosing collection method-Select the collection method according to the problem situation.', 'Searching for meaning of data-Understand what the analyzed data mean..', 'Using various expression methods-Use a variety of expression tools.' using the backward design model to integrate education, class, and assessment. As a result, the detailed competencies of data collection, analysis, and representation and achievement standard were presented. This may help in setting specific and specific criteria for what direction classes are recommended when planning data-related classes in elementary schools.

• 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.