• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.105-120
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• 2019

The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

• Journal of Educational Research in Mathematics
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• v.19 no.2
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• pp.289-306
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• 2009

In this study, pre-service teachers who would be mathematics teachers of secondary school wrote the journal about learning mathematics. Journal writing helped pre-service teachers' math learning. Pre-service teachers could think mathematics reflectively, inquire it, represent their affectivity, plan the self-directed learning, and have the records of learning. They considered the educational issues as instructional methods, organization and management of instruction, assessment, and attitude of teacher. As well, they thought that journal writing was important in learning mathematics and how they would apply their students. Journal writing was meaningful to pre-service teacher education in cognitive and situative aspects.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.485-491
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• 2010

The arrival of the Web 2.0 age, which is characterized by such key words as participation, sharing and openness, provides a learning environment in which both instructors and students can experience interactivity. In the educational area, we are particularly witnessing a growing interest in the social software like Wiki as one of the communication tools that reflects the characteristics of Web 2.0 and focuses on the interactivity with others. Based on this background, this study aims to examine into the meanings of interactivity inherent in the Wiki-based learning by studying such cases where Wiki is being used as a learning tool. For the purpose of our study, we practiced the Wiki-based learning method on the study subjects of the 17 junior students from U- University and 18 junior students from K- University during their 2009 fall semester teacher training courses. Through a comprehensive analysis of the questionnaires, interviews, Interactivity Measurement Diagram, examinations on the Wiki uses, Daily Self-reflection Records, and any other materials collected throughout the program, we could garner the following results: First, most of the students acknowledged that the use of Wiki was a useful communication means and helped promote their interactivity during their learning activities. Second, the interactivity of the Wiki-based learning was found to be more dynamic in the team-based projects or the community-based Wiki uses than in the instructor-oriented cases. Third, the Wiki-based learning is judged effective in expanding the scope of thinking and improving the learning capabilities through the collaborative knowledge-building process. The educational employment of the social software like Wiki in this web 2.0 age has great potentials for the true establishment of the learner-oriented learning environment, which has long remained at a standstill.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• Journal of Engineering Education Research
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• v.14 no.1
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• pp.40-45
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• 2011

This study was conducted to compare the creative problem solving styles and conflict management types between engineering college students with high and low team performance records. Most students with high team performance records preferredDeveloper style(76.7%) and Task-oriented style(65.1%), whereas most students with low team performance records preferred Explorer style(72.2%) and Person-oriented style(86.1%). Results of the comparison of conflict management types revealed that most students with high team performance records belonged to a competitive logic type(22.8%) and an accommodative compromise type(17.7%), while most students with low team performance records belonged to an accommodative compromise type(20.3%) and a permissive resignation type(12.7%). It would be useful to provide the engineering students with the specialized program to complement their problem solving styles and conflict management types.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.125-137
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• 2005

The digital archives of E-culture, E-government, E-learning, and E-business have grown by leaps and bounds worldwide during the last several years. While we have invested significant time and effort to create and maintain those archives, we do not have the ability to make digital records generated by the processes all available across generations of information technology, making it accessible with future technology and enabling people to determine whether it is authentic and reliable. This is a very serious problem for which no solutions have been devised yet. This paper discusses practical technologies for digital archives and preservation to succeed, and describes a general framework of the life cycle of information to address this important problem so that we may find reasonable ways to preserve digital records that can be analyzed and evaluated in quantitative measures and incremental manners.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.4
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• pp.149-182
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• 2018

In the era of digital transformation, new technologies have begun to be applied in the field of records management, away from the traditional view that emphasized the existing institutional and administrative aspects. Therefore, this study analyzed the service status of archives, libraries, and museums applied with advanced intelligent technology and identified the differences. Then, we proposed how to apply intelligent archival information services based on the analysis results. The reason for including libraries and museums in the research is that they are covered by a single category as an information service provider. To achieve our study aims, we conducted literature and case studies. Based on the results of the case study, we proposed the application strategies of intelligent archival information services. The results of this study are expected to help develop intelligent archival service models that are suitable for the changed electronic records environment.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.1-18
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• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.