• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.90-104
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• 2020

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users' satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users' understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.

• Cross-Cultural Studies
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• 제19권
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• pp.411-444
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• 2010

This paper explored the academic socialization of a group of Mongolian college students, learning Korean as their L3 (Third Language), by focusing on their uses of an electronic communication channel. From a perspective of the continua of bi-literacy, this case study investigated how Mongolian students who had limited exposure to a Korean learning community overcame academic challenges through the use of a written communication channel as a tool in the socialization process. Data were collected mainly through three methods: written products, interviews, and questionnaires. The results from this study were as follows. Interactional opportunities for these minority students were seriously constrained during the classroom practices in a Korean-speaking classroom. They also described the lack of communicative competence in Korean and the limited roles played by L2 (English) communication as key barriers to classroom practices. However, students' ways of engaging in electronic interactions differed widely in that they were able to broaden interactional circles by communicating their expertise and difficulties with their Korean peers through the electronic channel. More importantly, the communication pattern of "L2-L2/L3-L3" (on a L2-L3 continuum) emerging from data demonstrated how these students used a written channel as a socialization tool to mediate their learning process in a new community of learning. This study argues that a written communication channel should be taken as an essential part of teaching practices especially for foreign students who cannot speak Korean fluently in multi-cultural classes.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.175-179
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• 2021

Computer Science Unplugged activities are activities to learn about computer science through learning tools other than programming programs. Existing unplugged activities focus on the procedural thinking process and focus on guiding the thinking process through play. There is a lack of research on unsupervised learning, which plays an important role in machine learning, which has recently attracted attention. In this study, we designed and conducted an unplugged activities for unsupervised learning that analyzes data using video media familiar to elementary school students. The results on the effectiveness of the class were analyzed using the bebras challenge. As a result of analyzing the scores of the pre-test and post-test, it was confirmed that the students' computational thinking and problem-solving ability improved.

• Proceedings of the Korea Contents Association Conference
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• 한국콘텐츠학회 2009년도 춘계 종합학술대회 논문집
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• pp.45-50
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• 2009

The educational exchange through e-learning is working very well in such case as develop e-learning, development of various learning tools, cooperative practical use of e-learning contents, etc. However because there were no considerations of LMS(Learning Management System) interconnection when each systems were developed, the exchange through e-learning is starting to raise a problem. Especially the exchange through e-learning between university produced problem for a variety of reasons by absence of direct exchange in every case such as communication of students information, communication of lecture information, etc. Hence in this thesis, I will present designed model about efficient LMS interconnection through analysis case of exchange through e-learning and deduce problem. In the first place I define essential part for study such as lecture establishment data, lecture data, user data, class data, student learning tracking to interconnection data, then constituted data interconnection table used view by data interconnection prcess. By experiment result, the accessibility between students and professors was more convenience, and decreased work process by less data exchange. Henceforth there are researches in development of various essential parts for study, considered security of LMS interconnection.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.75-77
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• 1998

Perhaps one of the most versatile approaches to learning in practical domains lies in case based reasoning. To date, however, most case based reasoning systems have tended to focus on relatively simple domains. The current study involves the development of a decision support system for a complex production process with a limited database. This paper presents a set of critical issues underlying CBR, then explores their consequences for a complex domain. Finally, the performance of the system is examined for resolving various types of quality control problems.

• The Journal of Korean Academic Society of Nursing Education
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• 제27권1호
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• pp.80-90
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• 2021

Purpose: This study aimed to investigate nursing students' learning agility and confirm the mediating effect of self-leadership in the relationship between achievement motivation and learning agility. Methods: The study design was a descriptive survey design. The subjects were third- and fourth-year nursing students attending three universities in one region. Data were collected from November 28, 2019, to May 25, 2020, and a total of 202 data were collected using the scale of achievement motivation, self-leadership, and learning agility. Data analysis included frequency analysis, descriptive statistics, and Pearson's correlation coefficient using SPSS 25.0 statistics 25.0 software. The mediating effect of self-leadership was analyzed through regression analysis and bootstrapping using process macro ver. 3.4.1. Results: Self-leadership's partial mediating effect was confirmed in achievement motivation and learning agility. Achievement motivation was found to affect directly learning agility, with an indirect effect through self-leadership. Conclusion: The study results showed that nursing students could increase their learning agility through self-leadership improvement. Future research should focus on identifying the factors influencing nursing students' learning agility and develop and apply programs to improve learning agility.

• Korean Chemical Engineering Research
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• 제58권4호
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of The Korean Association For Science Education
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• 제30권2호
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• pp.218-233
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• 2010

The purpose of this study was to explore science teacher's epistemological understanding of science and science teaching and learning, from the perspective of inquiry as the process of scientific knowledge building. Three science teachers participated in this study. The data were collected from individual in-depth interviews and classroom videotaping. The results show a case involving coherent and consistent data. It showed that the teacher's epistemological understanding of science and science teaching and learning consisted of five categories: scientists doing science with scientific thinking; scientific thinking as the process of knowing; science learner in the learning process of scientific thinking; science teacher as a man/woman with good understandings of science; and teaching and learning as the process of knowing science. Based on the results, discussions and implications about science education and science teacher education were presented.

• Journal of IKEEE
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• 제20권1호
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.