• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.141-151
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• 2022

Recently, basic literacy education related to digital literacy and data literacy has been emphasized for students who will live in a rapidly developing future digital society. Accordingly, demand for education to improve big data and data literacy is also increasing in general universities and universities of education as basic knowledge. Therefore, this study designed and applied big data analysis courses for pre-service teachers and analyzed the impact on data literacy. As a result of analyzing the interest and understanding of the input program, it was confirmed that it was an appropriate form for the level of pre-service teachers, and there was a significant improvement in competencies in all areas of 'knowledge', 'skills', and 'values and attitudes' of data literacy. It is hoped that the results of this study will contribute to enhancing the data literacy of students and pre-served teachers by helping with systematic data literacy educational research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.83-92
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• 2023

The purpose of this research is to develop a creative education program that utilizes AI education program based on big data for general education high schools, and to investigate its effectiveness. In order to achieve the purpose of the research, we developed a creative education program using artificial intelligence based on big data for first-year general high school students, and carried out on-site classes at schools and a validation process by experts. In order to measure the creative problem-solving ability and class satisfaction of high school students, a creative problem-solving ability test was conducted before and after the program application, and a class satisfaction test was conducted after the program. The results of this study are as follows. First, AI education program based on big data were statistically effective to improve the creative problem solving ability according to independent sample t test about 'problem discovery and analysis', 'idea generation', 'execution plan', 'conviction and communication', and 'innovation tendency' except 'execution', 'the difference between pre- and post-scores of male student and female student' on first year high school students. Secondly, in satisfaction conducted after classes of AI education program based on big data, the average of 'Satisfaction', 'Interest', 'Participation', 'Persistence' were 3.56 to 3.92, and the overall average was 3.78. Therefore, it was investigated that there was a lesson effect of the AI education program based on big data developed in this research.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.543-552
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• 2022

This study tried to analyze the social perception of news coverage on inclusive education by using big data analysis techniques. News articles were collected according to the 5-year policy period for the development of special education, and news big data was analyzed. As a result, the frequency of media reports during the five-year policy period of special education development from 1998 in the first year to 2022 in the fifth year was steadily increased. During this period, the top topic words in news coverage changed from words conceptualizing simple definitions to words expressing the active will of students with disabilities for the actual right to education. In addition, as a result of emotional analysis of the overall keywords in the inclusive education news coverage, it was found that the positive word ratio was high. Through this study, it can be seen that interest in news coverage on inclusive education is increasing quantitatively in accordance with changes in special education policies, and the demand for inclusive education is being concreted in the direction of guaranteeing the actual right to education of students with disabilities.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.381-388
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• 2023

This thesis is purposed to develop a regional-customized education program selection model using big data analysis. Based on the literature review, the concepts and characteristics of big data and lifelong education are analyzed. In addition, this thesis presents how to collect the data for lifelong education and to use big data suitable for the characteristics of lifelong education. Based on these results, a regional- customized lifelong education program selection model is developed. The regional customized lifelong education program model is developed by the following six steps. The customized education program model proposed in this study has a high degree of flexibility in terms of practical use, as it can be utilized in real-time data provision methods such as the nationally approved Lifelong Learning Personal Status Survey without the need for analysis one year later, allowing for selective analysis and future predictions. It is clear that there is a significant need and value for big data in the education field. Furthermore, all programs used in the sample model are provided free of charge, and due to the programming nature, the community is actively engaged in exchanges, making it very easy to modify and improve for the development of a more complete education program model in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1562-1565
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• 2020

With the development of digital technology, the industrial structure is becoming digitalize. The government selected big data as the key technology of the 4th industrial revolution. Among them, big data is widely used to create new values and services by utilizing vast amounts of information. In order to cultivate professional manpower for the use of big data, various education programs are provided at universities. We intend to develop a curriculum for systematic training of talented people who can acquire knowledge about the three stages of collection, analysis, and application of big data. To this end, subjects are classified into basic competency, technical competency, analysis competency, and business competency based on the big data competency model proposed by the Korea Internet & Security Agency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1073-1079
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• 2014

One of the techniques that are most in the spotlight today, it can be said that Big data. With Big Data, technologies already prevalent in our lives is GPS. Based on the GPS data and Big Data, in this paper, we try to analyze the pattern and path of movement of a particular target. Specific target collects the GPS data by classifying weather and grade and sex of college students, and day of the week in college students of one university. The collected data is analyzed such as movement path, movement time, pattern of repetitive behavior. And visualize it. The analysis method will be classified according to the purpose of data. By identifying relationships with other data results obtained. Based on the present study, the future, we will derive the results of the data more reliable. For this purpose, a wide range of information to be collected will additionally. Research will be developed add to such as Season, time, blood type, occupation data.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.332-335
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• 2018

In recent years, artificial intelligence using big data has become a big issue in IT. Various studies are being conducted on services or technologies to effectively handle big data. The educational field, there is big data about students, but it is only a simple process to collect, lookup and store such data. In the future, it makes extensive use of artificial intelligence, machine learning, and statistical analysis to find meaningful rules, patterns, and relationships in the big data of the educational field, and to produce intelligent and useful data for the actual students. Accordingly, this study aims to design a program to predict the career of students using a decision tree algorithm based on the data from the student's classroom observations. Through a career prediction program, it is believed to be helpful to present application paths to students ' counseling and to also provide classroom behavior and direction based on the desired courses.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.157-164
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• 2019

Big data is a field that has been utilized and developed in various fields in our society recently. Big data analysis techniques are frequently used to analyze various big data in various fields of politics, economy, and society to grasp various meanings hidden in the data. However, big data analysis is used some case studies of in fields of analysis of educational data, but analysis of the curriculum and direction is still inadequate. Therefore, this study aims to identify and analyze the core concepts of middle school informatics textbooks using big data analysis techniques. Text mining was used for big data analysis for informatics textbook analysis. Through the core concepts of middle school informatics textbooks identified using this techniques, we could confirm the concepts to be emphasized in the textbooks and the possibility of using big data in the field of education.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.