• Informatization Policy
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• v.30 no.1
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• pp.67-88
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• 2023

The expansion of the scope of product liability is necessary because the industrial environment has changed following the enactment of the Product Liability Act. Unlike human-coded algorithms, artificial intelligence is black-boxed according to machine learning, and even developers cannot explain the results. In particular, since the cause of the problem by artificial intelligence is unknown, the responsibility is unclear, and compensation for victims is not easy. This is because software or artificial intelligence is a non-object, and its productivity is not recognized under the Product Liability Act, which is limited to movable property. As a desperate measure, productivity may be recognized if it is stored or embedded in the medium. However, it is not reasonable to apply differently depending on the medium. The EU revise the product liability guidelines that recognize product liability when artificial intelligence is included. Although compensation for victims is the value pursued by the Product Liability Act, the essence has been overlooked by focusing on productivity. Even if an accident occurs using an artificial intelligence-adopted service, however, it is desirable to present standards according to practical risks instead of unconditionally holding product responsibility.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.127-132
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• 2023

The rapid development and progress of information and communication technology are bringing about changes in the field of education, and innovative methods in teaching-learning have been introduced and applied a lot in the actual field starting with the pandemic. Reflecting this trend, the purpose of this study is to examine the perception of prospective elementary school teachers on the use of edutech, which is widely used in the educational field after COVID-19. To this end, I analyzed the results of 7 major ideas selected at the Edutech Utilization Competition held by K University of Education and the focus group interview conducted with 2 pre-service teachers. As a result of the analysis, it was found that prospective elementary school teachers valued the necessity and importance of edutech-based classes. In addition, they were positively aware of the educational effect of edutech-based classes. To sum up, it could be predicted that edutech classes are also linked to the policies and plans of the Ministry of Education, and will play an important role in effectively guiding future learners while achieving the goals set by the curriculum.

• Journal of Korean Library and Information Science Society
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• v.51 no.3
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• pp.81-102
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• 2020

The purpose of this study is to examine the students' and teachers' perception about the roles and qualifications of teacher librarians. For this purpose, data were collected through survey from students and teachers at secondary schools and the data were analyzed by semantic network analysis. The results of the research are as follows: First, students usually perceived the role of teacher librarians as 'library management', and teachers did as 'reading education'. Second, among the roles of teacher librarians, it was confirmed that students' and teachers' perceptions of 'information literacy instruction and library instruction' were very weak. Third, while the students' perception of the role of a teacher librarian as a 'teaching collaborator' such as 'teaching and learning support' and 'library assisted instruction and collaborative instruction' was weak, teachers recognized the role of teacher librarians as 'teaching collaborators'. Fourth, students and teachers perceived high levels of 'information service', which consists of 'book recommendation and guide activities'. Finally, it was investigated that 'professionalism' plays a central role in the students' and teachers' perception about the qualities of teacher librarians. These results can be used to establish the role of teacher librarians, develop response strategies for students and teachers, and improve their awareness.

• The Journal of Korean Association of Computer Education
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• v.21 no.3
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• pp.11-20
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• 2018

One major change in the 2015 revised national curriculum is that computational thinking ability is becoming an essential competency for students. Computational thinking is divided into abstraction, automation, and creative convergence in the curriculum for secondary schools' Information subject. And, the curriculum contains problem solving and programming area. Among the components of computational thinking, data representation emphasizes the ability to structure data and information for problem solving of learners. Pre-service teachers of Information subject at secondary schools also learn how to structure information through diagramming. There are differences in the ability to structure diagrams among students, but the studies on learning methods that help students develop their structuring abilities have rarely been performed. The purpose of this paper is to analyze the differences of abstract thinking disposition and gender perspective among college students. As a result, female students had more concrete thinking disposition than male students. Also, there were gender differences according to the characteristics of diagrams. Differences in abstract thinking disposition also made a difference in structuring diagrams. It is useful for achieving the education purpose of improving computational thinking ability by finding out the differences in thinking tendency between males and females and finding the education method that can complement them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korean Library and Information Science Society
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• v.52 no.2
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• pp.45-64
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• 2021

This study compares the members of children's reading and culture movement organization versus general public and their children with the aim of understanding how parents' reading activities affect children's reading activities. The study surveyed 477 elementary school students and 483 parents from five special metropolitan cities regarding their reading behaviour. Reading behavior was investigated in terms of reading frequency, book selection information source, reading awareness, and preferred books, and it was confirmed that there was a difference between members of children's reading and culture movement organization and children of the general public. Members of children's reading and culture movement organization and their children showed superior reading habits in terms of both quantity and quality than non-members and their children, and the book selection information service was used. In terms of perception regarding reading, children's reading and culture movement organization members and their children found more "joy" in reading than "help in learning" as compared to the general public and their children. In terms of reading preference, children's reading and culture movement organization members and their children intensively preferred Korean creative fairy tales and picture books while the general public and their children preferred Korean creative fairy tales, picture books, and educational comics. In order to create a healthier reading culture and environment for the long term, the development of more active reading participation methods for the general public is required.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.107-122
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• 2014

The purpose of this study is to investigate trends in research studies on scientific argument and writing in Korea. 118 research studies published from 2004 to 2013 have been collected and analyzed. Many of the research studies focused on developing teaching strategies, analyzing contents of scientific argument and writing, and effects on student learning. More than half of the studies were conducted with elementary and middle school students while studies with pre-service, in-service teachers or high school students were relatively rare. Most research studies were conducted within regular school hour context and participants were given relevant information/education prior to argument and writing activities. Many research have analyzed student growth in scientific attitudes and we would suggest that further studies should investigate student growth in scientific concepts, scientific inquiry, and critical thinking. The structure and process of argument or the content and form of writing have been analyzed. The quality of argument and scientific concepts embedded in argument and writing should be investigated more in future researches.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.285-296
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• 2004

The rapid progress of IT technologies promoted the foundation to offer users 'Any Time, Any Where, Any Service', and wireless internet services made it possible to use wired internet services while traveling. The previous academic administration management system having migrated from wired to wireless was dependent on mobile equipments' platform because of not being constructed on standard surroundings. And in the aspect of faculty system, course coordinator plays an significant role in building curricula and manage them, and finally counseling students with regard to them. But the course coordinator can't afford to advise students on which fields of their faculty fit them and which courses they have to take. We propose a mobile course coordinator system to help students learn profound courses of their major fields. Also the proposed system is implemented by using JAVA and WIPI technology, so that it is platform-independent. A mobile course coordinator system has an inference engine considering not only course trees which tell informations about the courses in every fields, but also personal courses that students have taken. The inference engine calculates three weights, representing the significance of each course considering every fields, the score of prerequisite courses which a student have taken, and the suitability in which department each student fits. When students apply for taking lectures, a mobile course coordinator system recommends them the most suitable courses. A mobile course coordinator system is able to substitute for the course coordinator who is counseling students. And the students with personal cellular phone are able to keep tracking their courses, and improve their knowledge about major with taking courses which the system's inference engine will advice.