• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1208-1213
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• 2018

The study investigates how the structured debriefing method affects the learning flow, critical thinking disposition, and clinical performance of nursing students, using the Lasater Clinical Judgment Rubric (LCJR). Nursing students in the 4th grade of P University were divided into three groups, each trying out a different structured debriefing method: the experimental group - structured video debriefing using the LCJR question, the comparative group - structured oral debriefing, and the control group - structured group discussion debriefing. There was no significant difference between the three groups in learning flow (p=.640), critical thinking disposition (p=.420) and clinical performance ability (p=.360). Planning and intervention among the areas of clinical performance were significantly improved in the experimental group compared to the other two groups (p=.005). Structured debriefing when used with LCJR improves the learning flow and critical thinking disposition of students, while structured video debriefing improves clinical performance.

• The Journal of Information Systems
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• v.17 no.4
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• pp.31-49
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• 2008

As the development of information technology, the biggest change in educational paradigm is apparent in the shift that the emphasis of education is layed on from teachers to learners. E-learning education service through the internet is less restricted in the respect of time and places in comparison with off-line education. Therefore e-Learning is spreaded rapidly and the educational effectiveness of that is needed to be investigated. In this study theoretical research was performed firstly and framework of the study was constructed. After establishment of hypotheses the survey data were collected by the learners of e-Learning and the hypotheses were verified by the SPSS version 12.0. The results are as follows : First, the quality of e-Learning service influences significantly to the technology acceptance of users. Secondly, perceived usability and perceived easiness of technology acceptance model influences significantly to the intention of reuse of users of e-Learning services. Lastly, the playfulness of the Flow theory influences significantly to the intention of reuse of users of e-Learning services. Although there are some limitations in the respect of the numbers of variables, parameters, or samples, this study will contribute for enhancing the effectiveness of education in e-Learning service by providing the acceptance factors of e-learners.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.221-229
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• 2018

Machine learning and artificial intelligence are core technologies for the 4th industrial revolution. However, it is difficult for the general public to get familiar with those technologies because most people lack programming ability. Thus, we developed a Graphic User Interface(GUI) to overcome this obstacle. We adopted TensorFlow and used .Net of Microsoft for the develop. With this new GUI, users can manage data, apply algorithms, and run machine learning without coding ability. We hope that this development will be used as a basis for developing artificial intelligence in various fields.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.401-411
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• 2022

The purpose of this study was to identify the convergence factors influencing learning satisfaction of nursing students in the COVID-19 pandemic after applying non-face-to-face mixed classes consisted of both real-time and non-real time distance educations. The participants were 109 nursing students who attended in a university and completed the self-report questionnaire. Data were analyzed using the SPSS 23.0 program. The results showed that the learning flow was 3.41, self-regulated learning ability was 3.75, and learning satisfaction was 3.98. Learning satisfaction showed a positive correlation with learning flow (r=.42, p<.001) and self-regulated learning ability (r=.75, p<.001). In addition, the factors influencing the learning satisfaction of the subjects of this study were self-regulated learning ability (𝛽=.662) followed by 60.6% (F=25.63, p<.001). Therefore, to enhance learning satisfaction of nursing students, it is necessary to increase their self-regulated learning abilities and to develop and apply training program considering the needs of the educational environment change in the post-COVID-19 era.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.525-531
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• 2011

This study is about the system for knowledge management in the Wiki-based project learning. We implement the Wiki-based project learning system which is focused on a new Web paradigm and technology development to grasp the knowledge flow of a learner effectively under a project learning condition. Implementation of the system has used a Web 2.0 technology to easily understand SECI Knowledge Management types which form the Externalization, Combination and Internalization steps. Moreover, the system structure has been designed instinctively for harmonious knowledge use or reuse. As a result of the experiment, we found out that the collaborative knowledge steps moved along the flow of project learning.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.77-88
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• 2007

More flexible learning models are needed, and learning environments that operate through mobile technologies such as portable multimedia players(PMP) provide useful tools in implementing these learning models. The main attractant of PMP is often their versatility: being able to load and play different formats of video, audio, digital images, and interactive media. In this paper, we investigate the factors influencing the usage and acceptance of the PMP for study, based on the extended version of the Technology Acceptance Model (TAM). Based on data collected from online survey, we show that perceived usefulness, perceived ease of use, flow and perceived enjoyment are the major determinants for users to play PMP for study purpose. Factors, including ease of use, contents-credibility are shown to determine the level of perceived usefulness; additionally, perceived usefulness, ease of use and perceived enjoyment are shown to directly affect the level of flow. Based upon the statistical results, some useful guidelines for developing learning contents are also provided.

• Journal of architectural history
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• v.13 no.4 s.40
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• pp.7-18
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• 2004

From the results of an examination of the transition process of the site plan divided into 5 stages based on literature and materials relating to the Sangju Confucian School as well as the construction history, we can see the general transition flow as follows. The arrangement form of Sangju Confucian School shows the structures with both the sacrificial rites function and the learning function in the early period. This shows a large general flow where the form with the learning function structure at the front and sacrificial rites function structure at the back changed to a form where the learning function structure was positioned behind the boarding facilities, after which there was a transformation which left only the learning function (the form where the learning function structure was positioned in front of the boarding facilities). The type where the learning function structure is positioned in front of the boarding facilities is hard to find in the Yeongnam area, also, there are not many examples of the 2 story Myeonglyundang (hall of confucianism teachings) throughout the country Sangju Confucian School which possess the value of rarity is appraised as being a precious material showing another area characteristic in Sangju of the Yeongnam area. Also, during the late Chosun period the scale of the Dongseojae (boarding facility) was reduced and the appearance of Yangsajae can be said to be a typical example of confucian school constructions of late Chosun era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5135-5142
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• 2011

A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.