• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.71-90
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• 2010

Electronic learning(or e-learning) has created hype for companies, universities, and other educational institutions. It has led to the phenomenal growth in the use of web-based learning and experimentation with multimedia, video conferencing, and internet-based technologies. Many researchers are interested in the factors that affect to the performance of e-learning or e-learning services. In this sense, this study is aimed at proposing e-learning system reuse prediction models in which e-learner intention to reuse influence factors(i.e., system accessibility, system stability, information clarity, information validity, self-regulated efficacy, computer self-efficacy, perceived usefulness, perceived ease of use, flow, and parental expectation) affect e-learner intention to reuse positively. A web survey was conducted for the full members of the e-learning education institute A in Seoul, Republic of Korea, an exclusive e-learning company that provides real time video lectures via the desktop conferencing system. The web survey was conducted for 20 days from November 5, 2009, through the e-learning web site of the company A. In this study, three data mining techniques were used : the multivariate discriminant analysis, CART, and C5.0 algorithm. This study was conducted to provide the e-learning service providers, e-learning operators, and contents developers with marketing and management strategies for improving the e-learning service companies, based on the data mining analysis results.

• Journal of KIBIM
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• v.10 no.4
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• pp.60-69
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• 2020

Allowing on-site workers to access information of the construction process can enable task control, data integration, material and resource control. However, in the current practice of the construction industry, the existing methods and scope is quite limited, leading to inefficient management during the construction process. In this research, by adopting cutting edge technologies such as Augmented Reality(AR), digital twins, deep learning and computer vision with wearable AR devices, the authors proposed an AR visualization framework made of virtual components to help on-site workers to obtain information of the construction process with ease of use. Also, this paper investigates wearable AR devices and object detection algorithms, which are critical factors in the proposed framework, to test their suitability.

• The Mathematical Education
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• v.45 no.3 s.114
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• pp.295-313
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• 2006

University teacher education programs have sought for ways of how to improve student teaching in order to supply mathematics teachers with practical theory to achieve the goals of the current educational reform in school mathematics. In this context, the purpose of this research is to investigate the effect of student teachers' teaching experience in the after-school mathematics programs and the ways of how to develop the after-school learning programs as an effective site for learning to teach based on the inquiry into student teachers' own teaching experience. For the purpose, data were collected through the interviews with the student teachers who had taught after-school mathematics class. In addition, data were collected through survey, class observation, and seminal meetings with the student teachers in order to supplement the findings from the interview analysis. Data analysis focused on the student teachers' experience with teaching in after-school mathematics classes, that is, what and how they had learned as teachers, what kinds of difficulties they encountered in their teaching and supports that they expect to improve their learning through teaching. The analysis shows that the teaching experience in the after-school programs had positively contributed to their development as future mathematics teachers. Specifically, the after-school programs provide the site for learning through teaching at the early stage of teacher education program. The after-school programs provided the students teachers for the opportunity to participate peripherally in educational practice of school. Through the participation, the student teachers developed positive attitudes toward teaching career and became to have more solid ideas about how to teach mathematics. Based on the analysis, this research provides following suggestions concerning how to improve student teaching. First, it is necessary to provide student teachers to participate into the practice of teaching at the early stage of teacher education programs. Second, it is important to give students teacher opportunity to participate in teaching at peripheral and legitimate positions. Finally, it is necessary to construct mentoring networks to support student teachers to move from a peripheral position toward a center of teaching practice.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.525-530
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• 2013

The importance of engineering design increases due to the expansion of engineering education certification. But there are not much teaching methods and examples of engineering design to be referred to the college classes. This paper introduces a new teaching and learning method of Action Learning adopted to a engineering design class in the Department of Architectural Engineering, J University in Korea. The class included a team project to find problems of facilities or safety management factors in a building construction site, and to provide the alternatives to solve the problems. The Action Learning helped to improve the learning effect of students and to increase the quality of the project deliverables.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Engineering Education Research
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• v.23 no.5
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• pp.61-67
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• 2020

In the associate degree curriculum of the department of mechanical engineering, the results of the study are presented on the structure and content of a subject based on the case-based learning method. As an case, equipment called a laminator that is actually used in the manufacturing site was selected. Class deals with specific engineering issues at each stage of laminator manufacturing (design-machining-assembly-measurement-maintenance) in connection with general engineering topics in prerequisites in the curriculum. Topics include tolerance fit, length measurement, assembly practice, measurement design and statistics of machine maintenance, etc. Courses that apply the case-based learning method may be included in the curriculum as complementary roles to those that apply other student-centered learning method.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.45-52
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• 2020

Since the opening of the national petition site, it has attracted much attention. In this paper, we perform topic analysis of the national petition site and propose a prediction model for answerable petitions based on deep learning. First, 1,500 petitions are collected, topics are extracted based on the petitions' contents. Main subjects are defined using K-means clustering algorithm, and detailed subjects are defined using topic modeling of petitions belonging to the main subjects. Also, long short-term memory (LSTM) is used for prediction of answerable petitions. Not only title and contents but also categories, length of text, and ratio of part of speech such as noun, adjective, adverb, verb are also used for the proposed model. Our experimental results show that the type 2 model using other features such as ratio of part of speech, length of text, and categories outperforms the type 1 model without other features.

• Proceedings of the Korea Society of Design Studies Conference
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• 2003.11a
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• pp.70-71
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• 2003

• Hwankyungkyoyuk
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• v.21 no.2
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• pp.89-102
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• 2008

Because of the rapid industrialization and urbanization, urban dwellers are lack of opportunity to contact with nature, and hence alienated from it. In particular, primary school children who are very sensible to nature need more opportunities to learn nature by direct interactions with it. For this purpose, a movement for making and using ecological learning-place in play-ground within primary school. It has been found as a result of research on ecological learning-places in 7 primary schools in Daegu that such places, equipped with several ecological facilities, provide both pupils and local dwellers around schools with a place for ecological learning and for rest. But some of them have been left without care and hence can not be properly used, because of inappropriate site, insufficient facilities, and deficient programme for practical use. In conclusion, the paper reconfirms importance of ecological learning-place within grounds of primary school in terms of its educational, social and ecological effects, and suggests briefly some measures to encourage its construction and practical use.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.