• International journal of advanced smart convergence
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• v.6 no.3
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• pp.22-28
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• 2017

With the recent development of technologies to manage vast amounts of data, data mining technology has had a major impact on all industries.. Data mining is the process of discovering useful correlations hidden in data, extracting executable information for the future, and using it for decision making. In other words, it is a core process of Knowledge Discovery in data base(KDD) that transforms input data and derives useful information. It extracts information that we did not know until now from a large data base. In the decision tree, c4.5 algorithm was used. In addition, the C4.5 algorithm was used in the decision tree to analyze the difference between frequency and mortality in the region. In this paper, the frequency and mortality of percutaneous coronary intervention for patients with heart disease were divided into regions.

• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.314-335
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• 2002

The purpose of this study was to investigate the effects of students' prior knowledge on scientific reasoning process performing a task of controlling variables with computer simulation and to identify a number of problems that students encounter in scientific discovery. Subjects for this study included 60 Korean students: 27 fifth-grade students from an elementary school; 33 seventh-grade students from a middle school. The sinking objects task involving multivariable causal inference was used. The task was presented as computer simulation. The fifth and seventh-grade students participated individually. A subject was interviewed individually while the investigating a scientific reasoning task. Interviews were videotaped for subsequent analysis. The results of this study indicated that students' prior knowledge had a strong effect on students' experimental intent; the majority of participants focused largely on demonstrating their prior knowledge or their current hypothesis. In addition, studnets' theories that were part of one's prior knowledge had significant impact on formulating hypotheses, testing hypothesis, evaluating evidence, and revising hypothesis. This study suggested that students' performance was characterized by tendencies to generate uninformative experiments, to make conclusion based on inconclusive or insufficient evidence, to ignore, reject, or reinterpret data inconsistent with their prior knowledge, to focus on causal factors and ignore noncausal factors, to have difficulty disconfirming prior knowledge, to have confirmation bias and inference bias (anchoring bias).

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.135-142
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• 2002

This paper aims to compare the effects of two teaching methods, the discovery-observational(DO) and the expository-observational(EO) instructions, on students learning interest in the life cycle of fruit fly. The subjects, 463 third-graders from two elementary schools in Changwon City, were divided into two groups, the DO group and the EO group. After the instruction on the life of the flies in two different teaching ways, a questionnaire with 13 items was devised regarding the students' interest, and the subjects were asked to respond to it. The results reveal that the general mean score of the DO group is higher than that of the EO group. Also, the DO group obtains the higher mean score in each item, except two items about knowledge learning. The differences of the mean scores of the two types, general as well as item-individual, between the two groups are statistically significant. This suggests that the class about the life cycle of living creatures easily getatable and observable, such as fruit flies, should be student-centered investigatory one, where students themselves collect them and observe the process of their growth and whole cycle.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1797-1808
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• 2021

Artificial intelligence is considered one of the core technologies leading the 4th industrial revolution. It is adopted in various fields bringing about a huge paradigm shift throughout our society. The field of biotechnology is no exception. It is undergoing innovative development by converging with other disciplines such as computers, electricity, electronics, and so on. In drug discovery and development, big data-based AI technology has a great potential of improving the efficiency and quality of drug development, rapidly advancing to overcome the limitations in the existing drug development process. AI technology is to be specialized and developed for the purpose including clinical efficacy and safety-related end points based on the multidisciplinary knowledge such as biology, chemistry, toxicology, pharmacokinetics, etc. In this paper, we review the current status of AI technology applied for drug discovery and consider its limitations and future direction.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.33-38
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• 2020

Modern society needs to think in new directions and solve problems by grafting problems from diverse fields with computers. Abstraction and automation of various problems using computing technology with your own ideas is called computational thinking. In this paper, we analyze how to diagnose and improve knowledge information based on computational thinking through the process of presenting a variety of problems in programming education situations and finding several problem-solving methods to solve them. To pretest the learners, they were diagnosed using a measurement sheet and a LightBot. By determining the correlation between the evaluation results and LightBot results, the learners' current knowledge statuses were checked, and the correlation between the evaluation results and the LightBot results, based on what was taught according to the problem-solving learning technique, was analyzed according to the proposed technique. The analysis of the group average score of the learners showed that the learning effect was significant. If the method of deriving and improving knowledge based on computational thinking ability for solving the problem proposed in this paper is applied to software education, it will induce student interest, thereby increasing the learning effect.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.113-129
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• 2009

Abstract Corporate activities are composed of numerous working processes and during the working flow, various business processes are being created and completed simultaneously. Enterprise Resources Planning (ERP) makes the working process simple, yet creates more complicated work structure and therefore, there is an absolute need of efficient management for business processes. The workflow literature has been looking for efficient and effective ways of rediscovering and mining workflow intelligence and knowledge from their enactment histories and event logs. As part of studies to analyze and improve the process, the concepts of 'Process Mining', 'Process re-discovery', 'BPR (Business Process Reengineering)' have appeared and the studies for practical implementation are proactively being done. However, these studies normally follow the approach throughout data warehousing for log data of process instances. It is very hard for these approaches to reflect user's intention to the rediscovering and mining activities. The process instances designed based on the consideration of analysis can make groupings effectively and when the analysis demand of user changes within the analysis domain can also reduce the cost of analysis. Therefore, the thesis proposes a special type of workflow model, which is called a colored workflow model, that is extended from the ICN (information control net) modeling methodology by reinforcing the concept of colored token. The colored tokens represent the conceptual types of constraints and criteria that can be used to classifying and grouping the workflow intelligence and knowledge extracted from the corresponding workflow models' enactment histories and event logs. Through the runtime information of process instances, it makes possible to analyze proactive and user-oriented process with the goal of deriving business knowledge from the beginning of process definition.

• Human Ecology Research
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• v.56 no.3
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• pp.301-315
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• 2018

This study explores the critical reflection process experienced by home economics preservice teachers during practicum. Data were collected in a critical analysis of class, practicum review, and journals written by sixteen preservice teachers. Text material were composed of 188 transcripts on A4 paper and 36 page of mini-notes. The collected data were analyzed by a thematic coding method in qualitative research and proceeded in the order of three steps of transference, coding, and subject discovery. The emerging themes were: 1) Observing class 2) Practicing class 3) Growth of class practice 4) Reflecting class. First, the observing class was an exploration process through the viewing of daily classes that involved the process of recognizing the classroom situation and various classroom contexts. Second, the practicing class was to strengthen the consideration of the class to form a relationship that could lead to learning in educational situations. Third, the growth of class practice was intended to recognize the orientation of the subject matter with pedagogical content knowledge. Four, the reflecting class was the process of experiencing practice with a continuous understanding of the class, class reflection, and changing the perspective from the current status. There is a part where critical reflection is difficult to be promoted deeply during 4 weeks; however, there was a possibility of a reflection practice that could promote achievement through the experience of a practicing class.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.61-70
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• 2002

The purpose of this study was to examine whether or not they have been revised corresponding to the purpose of revision by making a comparative analysis of the content and illustration in the 6th and 7th primary science textbooks. The analysis of content was composed of knowledge, inquiry process and attitude. The analysis of illustrations was composed of the kinds of illustrations and the role of illustrations. The findings of this research were as follows: First, as a result of content analysis of the primary science textbooks in the 6th and the 7th national curriculum, the ratio of inquiry process showed the highest frequency, next knowledge and lastly scientific attitude. And the 7th textbooks are greatly emphasized knowledge and science attitude. Second, as a results of the illustration analysis are as follows: There are conspicuous differences in that the illustration number of the 7th science textbooks Is about twice the illustration number of the 6th science textbooks and next, they place more weights on pictures and comics hard to discovery in the 6th science textbooks. Therefore, they have tried to induce the interests of students and heighten their understanding by supplementing the role of illustration presented as picture-centered and increasing its number presented as picture. In the results, although they have improved the problems of the 6th national curriculum a lot through the innovation including the interest induction of pupils through comics, the development of its content presentation method, the gradual change of subject number and the cultivation of curriculum according to the level of enrichment and supplementing types, the 7th science textbooks have fallen short of 6.3%, not around 30% in terms of the reduction in the 7th national curriculum. Accordingly, the 7th science textbooks also can be pointed out to have the problems of too much amount of studying compared to the time per week like the 6th science textbooks.

• Journal of The Korean Association For Science Education
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• v.16 no.2
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• pp.200-209
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• 1996

Recent literature in science education has emphasized nature of science in science teaching. The theme has been considered to be an important element for scientific literacy.The purpose of this study was to identify Korean science teachers' points of view on topics related to nature of science, such as definition of science, characteristics of scientific hypotheses, scientific theories and scientific laws, and their construction, scientists, and scientific methods. The relevant 13 items were selected from Views on Science-Technology-Society (VOSTS) by the authors for this study. Most teachers perceived science as an exploratory process or problem solving. Some perceived science as an application of knowledge to make this world a better place to live in. Teachers viewed scientific activities as scholastic and individualistic instead of pragmatic or collective. They did not hold clear understandings of the idea that scientific knowledge is subject to change. As identified in previous studies, teachers thought that scientific ideas develop from hypothesis to theories, and finally to scientific laws. They did not show sound understanding of inventiveness of scientific hypotheses and theories, nor discovery of scientific laws. In summary, teachers' major points of view reflected 'realism'. It suggested that they needed to understand nature of science in the ways which it has been described in recent literature of science education, in order to teach science with personal and social contexts.