• Title/Summary/Keyword: Web-Based Training

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The Effect of the Determinants of Distance-Learning on the Effectiveness of Education (E-learning의 결정요인이 학습효과에 미치는 영향)

  • Son, Dal-Ho;Kim, Hyun-Ju
    • Information Systems Review
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    • v.10 no.2
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    • pp.49-70
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    • 2008
  • The increase in demand for e-learning has created a need to explore the implications of the emerging paradigm shift on the learning environment. To utilize information technology to improve learning processes, the pedagogical assumptions underlying the design of information technology for educational purposes must be understood. However, little theoretical development or empirical research has examined the learning effectiveness in web-based distance learning. In this regard, the primary purpose of this study is to investigate which factors of E-learning influence the effectiveness of education and expectation. Based on the prior studies of the education and business training field, research model and research hypotheses were developed. Factors studied in this paper were student characteristics, system environment and teacher characteristics. The result showed that the student characteristics has the significant effect on the effectiveness of education and expectation. However, the system characteristics and the teacher characteristics have the partial significant effects. This result is partially due to the subject characteristics of this study, because the subjects of this study are the students and they have already the experiences in IT and e-learning.

Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base (지식베이스 구축을 위한 한국어 위키피디아의 학습 기반 지식추출 방법론 및 플랫폼 연구)

  • Kim, JaeHun;Lee, Myungjin
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.43-61
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    • 2019
  • Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

Improving the Accuracy of Document Classification by Learning Heterogeneity (이질성 학습을 통한 문서 분류의 정확성 향상 기법)

  • Wong, William Xiu Shun;Hyun, Yoonjin;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.21-44
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    • 2018
  • In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

Study on Compliance of Personal Health Record Application in Patients with Atopic Dermatitis (아토피피부염 환자의 개인별 증상 기록에 대한 순응도 연구)

  • Seo, Jin Soon;Kim, Young Eun;Kim, An Na;Kim, Ick Tae;Son, Yun Hee;Jang, Hyun Chul
    • Journal of Society of Preventive Korean Medicine
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    • v.24 no.2
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    • pp.71-82
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    • 2020
  • Objectives : The purpose of this study is to evaluate clinical utilization by measuring compliance with the use of mobile health applications (AtopyPHR developed in a previous study) for patients with atopic dermatitis. Methods : Based on the AtopyPHR and the input period and frequency survey results for each symptom item, a scenario for measuring compliance was derived. The study period was 4 weeks. Participants installed AtopyPHR app and Telegram app on their smartphones, conducted user training on the app, and recorded symptoms using the app for 4 weeks. At the 2nd and 4th week visits, the AtopyPHR data recorded by the user can be viewed on the web page and used for medical decision. Compliance was analyzed by the date the symptoms were recorded. Results : There were 28 participants, all (100%) were compliant, and the compliance was 96.8. The patients were 1 to 18 years old, and the average age was 8.2±5.7 years, 10 males and 18 females. The actual date of participation in recording symptoms was 28.6±0.56 on average. Compared to Week 1, compliance decreased at Week 2, and Week 4 had the highest compliance. Daily check, daily emotion, stool/urine/sleep, and meal management showed high compliance, SCORAD and quality of life were higher than required to record. Conclusions : AtopyPHR was effective in compliance. The results of this study could be used to collect personal health data in daily life through the AtopyPHR, improving participant compliance. It is considered to be meaningful because it measured the compliance with the symptom record actually recorded using the mobile app rather than a questionnaire. This study may be useful not only for personal health care but also for medical decisions, as opinions are given by experts who treat atopic dermatitis.

Development of Web Application Based on N-screen for Play Activities of Children with Developmental Disorder (발달장애 아동의 놀이 활동을 위한 N-스크린 기반의 웹앱 개발)

  • Kang, Jung Bae;Kim, Jin Hee;Kim, Chang Geol;Song, Beong Seop
    • Journal of Korea Society of Industrial Information Systems
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    • v.18 no.4
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    • pp.1-8
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    • 2013
  • In the modern society, instructional methods using diverse media have appeared thanks to the development of Information & Communication Technology, and applicability of such instructional methods has been fully corroborated. However, customized contents allowing for disabled children's environment are still insufficient. Hence, this study produced educational contents of play activities for children with developmental disability, through applying N-screen technology, IT technology that can provide the same contents via a variety of digital media. The produced contents allow programs to be set up according to a child's individual characteristics and be carried out anywhere and anytime via an Internet-enabled digital device. Further, the developed contents were produced so that they could be accessed from a child's various environment (home, school, etc.) via a PC, a smart phone, a portable from a child's various environment (home, school, etc.) via a PC, a smart phone, a portable device, etc. and that the same educational program could be conducted in linkage at home, school, etc. Three children with Intellectual disability and autism spectrum disorder were applied to the manufactured content. As a result, Content interaction between interaction between teachers and students in play training could use as a medium.In addition, the children's ability to select the appropriate components and reinforcements, special education professionals have used the content of the interviews are helpful in mediation than the existing content.

A Study on Spam Document Classification Method using Characteristics of Keyword Repetition (단어 반복 특징을 이용한 스팸 문서 분류 방법에 관한 연구)

  • Lee, Seong-Jin;Baik, Jong-Bum;Han, Chung-Seok;Lee, Soo-Won
    • The KIPS Transactions:PartB
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    • v.18B no.5
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    • pp.315-324
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    • 2011
  • In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.

Design and Implementation of Teaching-Learning System for ICT Underachivers (ICT 학습부진아를 위한 교수-학습 시스템의 설계 및 구현)

  • Jang, Jun-Hyung;Lee, Jae-Ho
    • Journal of The Korean Association of Information Education
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    • v.12 no.4
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    • pp.427-436
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    • 2008
  • It is more than 10 years since ICT training learning was introduced to educational curriculum, and it is now time for overall consideration about the result of the education. The most important difficulties that the teachers have are that there are big differences in the level of learning ability. The characteristics of ICT curriculum are its tool and stepwise progress. The main problem of a curriculum with such characteristics is with underachivers. To distinguish ICT underachivers, the present study was developed a distinction tool for investigation: The objects were the students in the 6th grade of the 4 elementary schools in Gyeonggi-do, and inquires were made to find out characteristics. Inquires were also made to the elementary school teachers in Goyang-city to find out the actual instructional situation. A teaching-learning system will be suggested to prevent the occurring of ICT underachivers by analyzing their characteristics. The system consists of a distinction examination module, a teaching-learning module and a feedback module, which are web-based, as well as an off-line actual class module. The purpose of the system is to prevent underachivers in ICT classes, so that the students' ability to utilize computer will be improved to a higher level.

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Current Roles and Administrative Facts of the Korean Physician Assistant (전담간호사 운영현황과 역할 실태)

  • Kwak, Chan-Young;Park, Jin-Ah
    • The Journal of the Korea Contents Association
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    • v.14 no.10
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    • pp.583-595
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    • 2014
  • Hospitals in Korea have been increasingly using physician assistants (PA) as an alternative way of dealing with the shortage of residents. However, some incidents of a Physician's Assistant practicing beyond their legal scope require closer examination of the current PA's roles and functions. This study is a web-based survey designed towards targeting physician assistants in Korea (KPA) who practice delegated tasks under a physician's license. Currently, there are 2,125 KPAs working in 141 general hospitals and medical centers. Data from 704 nurses from who responded to the questionnaire were analyzed with descriptive statistics using the SPSS 12.0 program. Their mean age is 32.5 years with 8-10 years of clinical experiences, with males being more likely to be a PA. Despite of KPAs providing medical services and performing invasive procedures, only 13% of KPAs are licensed APNs (advanced practice nurse). KPAs have a low job satisfaction due to a lack of rewards and the necessity for providing illegal practices, and are experiencing identity confusion. The current KPA system is a transitional product of the change from the hierarchial structure to a more collaborative relationship between the medical and nursing departments. Providing adequate education and training, establishing protocols with legal protection, and developing professional independent scope of care are recommended to deliver safe and efficient medical services.

CAgM, USDA and the National Drought Policy Commission Associated with WAMIS (농업기상웹서버관련 농업기상위원회, 농무성 및 한발정책위원회 현황)

  • Motha, Raymond P.
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.6 no.2
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    • pp.140-147
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    • 2004
  • Agrometeorological information is essential in many agricultural decisions if it reaches the user in a timely and appropriate manner. Agriculture is the backbone to local, regional, and global economic development. Thus, strengthening agrometeorological application to diverse agricultural sectors will benefit economic development. This paper discusses three distinct organizational minions that all share the same need for improved information technology. The World Meteorological Organization's (WMOs) Commission for Agricultural Meteorology (CAgM) has global responsibility for improved agrometeorological services of Members to aid agricultural production and to conserve natural resources. The United States Department of Agriculture, World Agricultural Outlook Board, publishes monthly World Agricultural Supply and Demand Estimates, considered to be a benchmark for both government and industry in production and trade decisions. The National Drought Policy Commission (NDPC), created by an act of the United States Congress, formulated a national drought policy based on preparedness rather than on crisis management. All three organizations recognize the need for IT applications in agricultural meteorology and have been active in implementing this technology. The development of information technology offers new means of dissemination of agrometeorological products. World Agrometeorological Information Service (WAMIS) has taken advantage of the global Internet application to offer WMO Members a dedicated web server to host agrometeorological bulletins and training modules.

Study on the modeling of human resource development in webtoon authors (웹툰작가의 인적자원개발 모델링 연구 : 창의인재동반사업을 중심으로)

  • Kang, Eun-won;Lee, Sung-jin
    • Cartoon and Animation Studies
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    • s.46
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    • pp.129-150
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    • 2017
  • With the change in educational environment of cartoon creation and diversification of webtoon platforms, various ways of engaging webtoon authors have been suggested. Under this situation, Korea Manhwa Contents Agency(KOMACON) and Korea Creative Content Agency(KOCCA) provide support to webtoon authors directly and indirectly to nurture professional webtoon talents. Contents creative human resource joint project being carried out by KOCCA is mainly to nurture and support contents experts by developing their creativity through tight training between mentors and mentees, creating job opportunities, building the support system for creative activities, and supporting commercialization during the project. Undergoing the process of recruitment and selection, the participants of this project are educated, trained and developed according to education programs provided by the hosting agency, and this project has a model to compensate for creative activities for a ceratin period of time. However, there has been a problem that it is difficult to constantly keep and manage webtoon talents who are cultivated by human resource management of less than one-year project. This study analyzed creative human resource joint project which is a human resource development model, using human recourse theory and suggested a strategic human resource model based on webtoon authors' human resource model development.