• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.623-625
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• 2018

As the issue of particulate matter spreads, services for providing particulate matter information in real time are increasing. However, when a sensor node for collecting particulate matter is defective, a corresponding service may not be provided. To solve these problems, it is necessary to predict and deduce particulate matter. In this paper, a particulate matter prediction model is designed using artificial neural network algorithm based on past particulate matter and meteorological data to predict particulate matter. Also, the prediction results are compared by learning the input data of the model in the design stage.

• Journal of Families and Better Life
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• v.27 no.2
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• pp.203-211
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• 2009

This study was conducted to evaluate a forecast model designed to describe a vocational high school for runaway students. The study included 2000 adolescents from the KEEP(Korean Education and Employment Panel). A Data mining decision tree model revealed that: (1) Suicide ideation was a risk factor for running away among smokers. (2) High self-evaluation was a risk factor for running away among individuals that smoked and had no suicide ideation. (3) Drinking was predicted as a risk factor for no smokers, while family life dissatisfaction was predicted as a risk factor among non-smokers that drank. (4) Negative relationship with mother was predicted as a risk factor among non-drinking non-smoking.

• Korean Journal of Health Education and Promotion
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• v.26 no.2
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• pp.49-61
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• 2009

Objectives: The purpose of the study was to test the applicability of the Theory of Planned Behavior(TPB) to Korean adult smokers in order to obtain an enhanced understanding of their smoking-cessation intention and to establish strategies for effective smoking-cessation interventions at the community clinic. Methods: The data came from a survey that was conducted on adult male smokers who visited the smoking-cessation clinic at H community clinic in Korea from January to November in 2006 (N=434). Included in the survey were questions asking core variables and salient belief variables dictated by TPB. A structural equation model was established using TPB and data were analyzed to examine the applicability of the theory to the understanding of the structure of variables leading to intention. Results: The analysis of the structural model revealed that the study model was statistically significant(p<.001), suggesting the high validity of TPB in explaining the structure of the data. Among the relationships in the model, negative behavioral beliefs were not significant(p>.05), and negative control beliefs and subjective norm were significant(p<.05) but not as highly as other variables in the model(p<.001). Conclusion: TPB was useful in understanding smoking-cessation intention of smokers who visited community clinic to stop smoking. More specific strategies for counseling and education need to be developed to enhance attitude and perceived behavioral control (self-efficacy) toward smoking-cessation intention, such as emphasizing health benefits of quitting smoking, and eliciting values and life principles that can help increase the intention to stop smoking.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.109-115
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• 2021

The number of freshmen at universities is decreasing due to the recent decline in the school-age population, and the survival of many universities is threatened. To overcome this situation, universities are seeking ways to use big data within the school to improve the quality of education. A study on the prediction of dropout students is a representative case of using big data in universities. The dropout prediction can prepare a systematic management plan by identifying students who will drop out of school due to reasons such as dropout or expulsion. In the case of actual on-campus data, a large number of missing values are included because it is collected and managed by various departments. For this reason, it is necessary to construct a model by effectively reflecting the missing values. In this study, we propose a university student dropout prediction model based on eXtreme Gradient Boost that can be applied to data with many missing values and shows high performance. In order to examine the practical applicability of the proposed model, an experiment was performed using data from C University in Chungbuk. As a result of the experiment, the prediction performance of the proposed model was found to be excellent. The management strategy of dropout students can be established through the prediction results of the model proposed in this paper.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.273-283
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• 2023

Education has recently witnessed a rapid increase in the use of edutech worldwide. This study focuses on Korean workers and explores an edutech-based learning model for vocational education and training. Based on analyses of edutech cases and interviews with edutech experts, a draft edutech model was designed and the validity was evaluated based on two Delphi surveys with a panel of experts in the field. The study's findings suggest that edutech-based employee education and training should prioritize LXP orientation (last CVR=1, last Mean=4.70) , implement adaptive learning through learning analytics (last CVR=1, last Mean=4.90), enhance the human touch effect using edutech (last CVR=1, last Mean=4.90), and emphasize the importance of designing curricula that apply edutech in a step-by-step learning process while incorporating suitable instructional design for the key technologies involved in vocational training programs. In addition, it was revealed that there is a strong need to implement a method that makes each stage of the learning process more effective (before, during, and after). Edutech-based vocational training program should consider the interests of all stakeholders, including learners, instructors, vocational training institutions, and government agencies. Given the promotion of government-sponsored vocational training projects in Korea, the findings of this research are likely to have significant implications for the future of Korea's education and training policies.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.4
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• pp.81-93
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• 2022

Objectives: The purpose of this study was to develop a model for comprehensively evaluating the risk of sarcopenia in Korean adults and to generate the sarcopenia risk scorecard model based on the results. Methods: The participants of the study were 7,118 adults without sarcopenia in the first basic survey, and a longitudinal analysis was conducted using data from the 1st to 8th survey (2006-2020) of the Korean Longitudinal Study of Aging (KLoSA). The data were analyzed using Rao-Scott chi-square test and weighted Cox proportional hazards regression of complex sampling design. The sarcopenia risk scorecard model was developed by Cox proportional hazards regression using points to double the odds (PDO) method. Results: The findings show that the risk factors for sarcopenia in Korean adults were gender, age, marital status, socioeconomic status, body mass index (BMI), regular exercise, diabetes and arthritis diagnosis. In the scorecard results, the case of exposure to the highest risk level was 100 points. The highest score range were given in the order of age over 65, low BMI, and low socioeconomic status. Conclusions: The significance of this study is that the causal relationship between various factors and the occurrence of sarcopenia in Korean adults was identified. Also, the model developed in this study is expected to be useful in detecting participants with risk of sarcopenia in the community early and preventing and managing sarcopenia through appropriate health education.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.25-30
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• 2021

With the introduction of AI education in the 2022 Revised Curriculum emphasizing the need for data related education, it is necessary to improve students' data based problem solving skills. This study seeks to study SW education methods to improve students' data based problem solving skills in accordance with these needs. Based on the ADDIE model, the demand analysis survey was conducted on teachers to analyze their needs. Based on the results of the demand analysis, we designed education programs under the theme of data based problem solving skills using microbit. In this study, we raise the importance of data based problem solving and the need for its capabilities. Subsequent studies need to reveal how data based problem solving SW education will demonstrate significant effects on problem solving skills.