• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.233-243
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• 2020

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.171-180
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• 2003

The recent paradigm of web-based teaching-learning is changing into a direction that analyzes the learning patterns of learners on the basis of learners' ability, aptitude, request, interest, learning history, activity profile, etc. and provides adaptive environment with individual learners The present study analyzed learners' learning patterns using data on learning activities and developed a personalization system that provides learning environment adapted to individual learners. This study customized in three aspects, which are recommendation of learning path, recommendation of interface and recommendation of interaction, through Web mining. The personalization system developed in this study was proved to be effective in improving individual learners' satisfaction with learning in Web-based teaching-learning environment.

• Journal of Information Science Theory and Practice
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• v.7 no.3
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• pp.6-20
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• 2019

Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.105-111
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• 2022

Multiple-choice survey types have been mainly performed to analyze students' needs for online classes. However, in order to analyze the exact needs of students, unstructured data analysis by answer for essay question is required. Big data is applied in various fields because it is possible to analyze unstructured data. This study aims to investigate and analyze what students want subjects or topics for software lecture in general education that process on non-face-to-face online teaching methods. As for the experimental method, keyword analysis and association analysis of big data were performed with unstructured data by giving a subjective questionnaire to students. By the result, we are able to know the keyword what the students want for software lecture, so it will be an important data for planning and designing software lecture of liberal arts in the future as students can grasp the topics they want to learn.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.299-307
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• 2012

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.73-83
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• 2020

Recently, the importance of self-directed learning has emerged in the fields of public education, private education, lifelong education, and vocational training education, in which learners can actively cope with knowledge in an infusion-oriented way. However, there are various theoretical knowledge such as concepts and strategies for self-directed learning, but the situation is insufficient for a system where learners can easily receive content in the academic field they want, depending on the actual self-directed learning operation plan or learning area. Therefore, since it is important to provide various learning content in this paper, we utilize text mining techniques to obtain appropriate information and refine and categorize the meaning. On-line, they want to study a system that provides a variety of content in the academic field that learners are trying to acquire.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.151-159
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• 2017

This study deals with characteristics and risk of a PM based on user survey result, road driving test and data analysis of PM accident. Text mining method is applied to extract PM accident data from Big Data, which are claim data of private insurance company. Road driving test and survey on safety, convenience, noise, overtake ability, steering ability, and climbing ability of PM are performed to evaluate user's safety and convenience considering domestic road condition. As the result of claim data analysis, annual average increase rate of PM accident is 47.4% and average compensation of personal mobility is higher than that of bicycle by maximum 1.5 times. 79.8% of PM accident is self-caused accident due to unskilled driving and age-specific diagnosis rate of driver over 60 is higher than that of under 60. Diagnosis rate of over 60 at lower limb, foot, rib and spine is especially higher than that of under 60. As the result of road driving test and user survey, satisfaction level on safety and convenience of PM is evaluated as close to that of bicycle and satisfaction level of PM is increased after boarding. Overtake ability, steering ability, and climbing ability of PM are evaluated as same or better than that of bicycle but warning equipment to pedestrian or bike such as horn is required because noise level of PM during driving is too low. Finally, user survey result shows that bicycle road is suitable for PM and safety standard, advance-education and insurance are required for PM. It is suggested that drivers' license for PM can be replaced by advance-education. Results of this study can be used to prepare safety measures and legal basis for PM operation.

• Journal of The Korean Association of Information Education
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• v.9 no.1
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• pp.113-124
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• 2005

In this study, we designed and developed a tool to help teachers select and develop effective performance assessment criteria considering characteristics of individual learners. Using this tool, we can analyze preferences of teachers and characteristics of students for each rubric by exploring the classification and association rules through data mining. Those findings can give us guidelines and insights for the development and the selection of performance assessment criteria. The classification rules found are used for the learner-centered evaluation reflecting learners' interests, capabilities, and circumstances. Association rules found are utilized for analyzing teachers' preference, which enable to reduce time and efforts for the development and selection of rubric. Also, this tool supports creation, change, and selection of teachers' rubric linked with the performance assessment of NEIS(National Education Information System).

• Fourth Industrial Review
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• v.3 no.2
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.