• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.634-638
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• 2014

The purpose of the study is to analyse students' academic achievement by their university major field and changes of grade. The subject was entering students of C medical graduate school in 2008. We divided entering students' major fields into two fields that are natural science and non-natural science filed. We analysed academic achievements of entering students by grades, curriculum from 2008 to 2011. And We analysed to find out whether there are differences in academic achievements by grades, curriculum of each major fields. There were no significant statistical differences in academic achievements by grade, curriculum of the two different university major fields. Futhermore, as a results of analysis on level(high, medium, low) distribution and differences of academic achievements by grade, curriculum of the two different university major fields, there were no statistically meaningful results. There is the need to keep entrance selection systems that open the possibility of selecting the students with other academic background. And there is the need to change general awareness assuming that there are differences in academic achievements by university major fields. We need to guide students with belief of their learning possibility.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.413-425
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• 2019

This study was conducted to identify factors affecting depression in junior nursing students(JNS). The number of the participants was 144 at a college in S and G city. The data were collected using questionnaires about somatic symptoms(SS), sleep quality, stress, adaptation to college life(AC), depression. Mean score of depression was 18.89, 58.3% are experiencing depression that requires clinical treatment. The significant predictors of JNS were levels of depression AC(β=-.503, p=.000) and SS(β=.263, p=.000) respectively, explaining 58.9% of variance. In order to control the depressive symptoms of JNS, it is necessary to diagnose basic learning ability from the beginning of admission and provide guidance management plans to help students adapt to academic activities by providing customized programs for each level to improve learning ability. It is necessary to develop and apply various intervention programs to alleviate physical symptoms such as fatigue/low energy experienced by JNS.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.99-104
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• 2018

Modern society has entered the era of the fourth industrial revolution beyond the information age. In other words, technology innovations such as life science, unmanned automobiles, drone, artificial intelligence, big data, robot technology, Internet of things, and nano-technology are leading the change of the world. In these technologies use and delivery of information is playing a key role, and the field of information security for the safe use of information has become an indispensable discipline. In this sense, it is necessary to standardize the curriculum of universities to foster security manpower to meet the needs of the era. In this paper, we develop and present a model to standardize the curriculum in the field of information security. Using this model, each educational institution will be able to select the necessary track or field to guide the students and cultivate information security manpower effectively.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.100-112
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• 2021

With the recent growth of the big data technology market, interest in visualization technology has steadily increased over the past few years. Data visualization is currently used in a wide range of disciplines such as information science, computer science, human-computer interaction, statistics, data mining, cartography, and journalism, each with a slightly different meaning. Big data visualization in smart cities that require multidisciplinary research enables an objective and scientific approach to developing user-centered smart city services and related policies. In particular, spatial-based data visualization enables efficient collaboration of various stakeholders through visualization data in the process of establishing city policy. In this paper, a user-centered spatial big data visualization expression request method was derived by examining the spatial-based big data visualization expression process and principle from the viewpoint of effective information delivery, not just a visualization tool.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of the Korean earth science society
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• v.22 no.5
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• pp.350-359
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• 2001

The purpose of this study is to develop theme-based integrated science unit by the interdisciplinary approach and to analyze it's effects on the science achievement and the attitude towards science learning. 'Interaction' and 'Stability' were selected as the integrated themes, and the main concept and subconcept in relation to the themes were extracted from the four areas of science, and the learning contents were constructed in the integrated ways. While the main concept have relevance to subconcept in the interaction, the main concept have little relevance to subconcept in the stability. Therefore, the stability was to fit with middle school integrated science theme, but the interaction was not. The theme-based integrated science units developed was implemented in middle school, and the results are follows. First, the science achievement of group of theme-based integrated science teaching is significantly higher than those of group of traditional teaching. Second, the scores of the test of attitude toward science learning of the group of theme-based integrated science teaching is significantly higher than those of group of traditional teaching. Third, the students' perception of theme-based integrated science teaching was positive. The students have participation, interest, motivation in theme-based integrated science teaching, and students have difficulty in learning theme-based integrated science teaching.

• Journal of The Korean Association For Science Education
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• v.25 no.7
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• pp.746-753
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• 2005

In order to be efficient teachers should understand the current level of leaners through diagnostic evaluation. However, it is arduous to administer a diagnostic examination in every class because of various limitations. This study examined, the major issues arising from the development of a new science diagnostic evaluation system by incorporating the using knowledge state analysis method. The proposed evaluation system was based on the knowledge state analysis method. Knowledge state analysis is a method where by a distinguished collection of knowledge uses the theory of knowledge space. The theory of knowledge space is very advantageous when analyzing knowledge in strong hierarchies like mathematics and science. It helps teaching plan through methodically analyzing a hierarchy viewpoint for students' knowledge structure. The theory can also enhance objective validity as well as support a considerable amount of data fast by using the computer. In addition, student understanding is improved through individualistic feedback. In this study, an evaluation instrument was developed that measured student learning outcome, which is unattainable from the existing method. The instrument was administered to pre-service physics teachers, and the results of student evaluation was analyzed using the theory of knowledge space. Following this, a revised diagnostic evaluation system for facilitating student individualized learning was constructed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.7-12
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• 2018

In the society where the paradigm of knowledge is rapidly changing and developing, convergence emphasizing the connection between knowledge and technology in various fields is significant. In order to cultivate these creative-convergent talents, STEAM(Science, Technology, Engineering, Arts, and Mathematics) is being considered important to make them equipped with creative thinking ability and core competence required in the future society and help them devise new ideas escaping from the branches of study. This study is about convergence instructional design of computer with marketing subject, which aims to foster talent. The results of this study are as follows. First, the structured process of convergence lessons. Second, the convergence lesson was based on a cyclic process with steps : selection of the subject concerned, selection of a topic, designing the lesson, mapping out the lesson plan and developing problems, having a final discussion on the whole lesson, performing the lesson and evaluating the lesson. Third, the development of the problems about the introduction of computer engineering and Internet marketing subject for convergence lessons. To make an effect of this model, studies applying this instructional design to many lectures should be implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.345-351
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• 2020

Administrative agencies today are paying keen attention to big data analysis to improve their policy responsiveness. Of all the big data, news articles can be used to understand public opinion regarding policy and policy issues. The amount of news output has increased rapidly because of the emergence of new online media outlets, which calls for the use of automated bots or automatic document classification tools. There are, however, limits to the automatic collection of news articles related to specific agencies or departments based on the existing news article categories and keyword search queries. Thus, this paper proposes a method to process articles using classification glossaries that take into account each agency's different work features. To this end, classification glossaries were developed by extracting the work features of different departments using Word2Vec and topic modeling techniques from news articles related to different agencies. As a result, the automatic classification of newspaper articles for each department yielded approximately 71% accuracy. This study is meaningful in making academic and practical contributions because it presents a method of extracting the work features for each department, and it is an unsupervised learning-based automatic classification method for automatically classifying news articles relevant to each agency.