• Journal of dental hygiene science
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• v.22 no.2
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• pp.126-129
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• 2022

Background: The problem with current dental hygienist education is that it operates as an education system based on the national examination rather than on a practical basis; thus, graduates have difficulties in practice after obtaining their license. This study aimed to propose a job-oriented curriculum by analyzing the links between the task analysis of Korean dental hygienists and dental hygiene learning goals. Methods: This study performed a relationship analysis based on a second job analysis study of dental hygienists conducted by the Korea Health Personnel Licensing Examination Institute and the learning goals of the Korean Dental Hygiene Faculty Association. Results: Based on the links between the task and learning goals of the dental hygienist, they were classified into six types: 1) tasks listed in the license exam and learning goal, 2) tasks not listed in the license exam but listed in learning goals, 3) tasks not listed in learning goals, 4) learning goals not related to tasks, 5) learning goals listed in a few tasks, and 6) tasks related to several learning goals. The results showed that most of them correspond to the 5th classification, followed by the 3rd and 4th categories, which are mostly basic science learning goals. Tasks without learning goals are not included in the curriculum; thus, the curriculum needs to be supplemented. The overlapping learning goals of several subjects for one job skill must be reduced in job-oriented education. Conclusion: We suggest that the dental hygiene curriculum be developed based on task analysis and reflected in the national dental hygienist exam. The clinical practice performance of dental hygienists will take further leap forward through task-oriented education.

• 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.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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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.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.3
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• pp.38-53
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• 2022

Recently, the acquisition and production of information using digital tools and the creation of new knowledge are emphasized as important educational elements. Therefore, in this study, the effect of learning achievement according to the digital literacy level of college students was analyzed. For the analysis, a questionnaire is conducted with college students majoring in aviation services attending universities in Seoul Capital Area and Chungcheong area. To verify the hypothesis of the study, demographic characteristics are identified based on the questionnaire, reliability and validity of measurement items are verified, and structural equation model analysis is performed to verify the hypothesis. The analysis results are as follows. First, among the sub-factors of digital literacy competency of college students majoring in aviation service, 'technology use' is found to have a positive effect on 'cognitive flow' and 'emotional flow' of learning flow except 'behavioral flow'. Second, among the sub-factors of digital literacy competency, 'self-learning' is found to have a positive effect on 'cognitive flow', 'emotional flow', and 'behavioral flow' in learning flow. Third, the sub-factors of learning flow, 'cognitive flow', 'emotional flow', and 'behavioral flow' have a positive effect on 'learning satisfaction'. Fourth, 'learning satisfaction' is found to have a positive effect on 'learning outcomes'. Based on the research results, practical support measures and strategies for educational success are presented.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1120-1126
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• 2000

Recently, in order to analyze the time series problems that occur in the nature word, and analyzing method using a neural electric network is being studied more than a typical statistical analysis method. A neural electric network has a generalization performance that is possible to estimate and analyze about non-learning data through the learning of a population. In this paper, after collecting weather datum that was collected from 1987 to 1996 and learning a population established, it suggests the weather forecasting system for an estimation and analysis the future weather. The suggested weather forecasting system uses 28*30*1 neural network structure, raises the total learning numbers and accuracy letting the selecting concentration learning about the pattern, that is not collected, using the descending epsilon learning method. Also, the weather forecasting system, that is suggested through a comparative experiment of the typical time series analysis method shows more superior than the existing statistical analysis method in the part of future estimation capacity.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.149-163
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• 2023

In this study we analyze research trends on machine learning healthcare based on papers from the United States, the United Kingdom, and Korea. In Elsevier's Scopus, we collected 3425 papers related to machine learning healthcare published from 2018 to 2022. Keyword frequency and centrality analysis were conducted using the abstracts of the collected papers. We identified keywords with high frequency of appearance by calculating keyword frequency and found central research keywords through the centrality analysis by country. Through the analysis results, research related to machine learning, deep learning, healthcare, and the covid virus was conducted as the most central and highly mediating research in each country. As the implication, studies related to electronic health information-based treatment, natural language processing, and privacy in Korea have lower degree centrality and betweenness centrality than those of the United States and the United Kingdom. Thus, various convergence research applied with machine learning is needed for these fields.

• Journal of Engineering Education Research
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• v.27 no.1
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• pp.32-40
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• 2024

This study attempted to develop an efficient management plan that allows both workers and organizations to coexist by analyzing the factors that influence the level of organizational immersion of engineering students. Analysis methods included frequency analysis, t-test, pearson correlation analysis, and hierarchical analysis. Firstly, self-esteem and transfer of learning were influential factors on organizational commitment. Second, self-esteem and transfer of learning were influencing factors of self-efficacy. Third, self-efficacy was an influential factor in organizational commitment. Fourth, self-efficacy appeared as a mediating effect on self-esteem and organizational immersion in learning transfer. Therefore, it is necessary to look for various factors that can increase self-efficacy, and to find opportunities for students to be highly immersed in the organization while studying at the same time.

• Educational Technology International
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• v.8 no.2
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• pp.53-71
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• 2007

This study identified the effectiveness of e-learning by comparing learning outcome in conventional face-to-face lecture with the selected e-learning methods. Two e-learning contents (animation based and video based) were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which is http://www.enaged.co.kr. Fifty-four Korean agricultural high school students were randomly assigned into three groups (face-to-face lecture, animation based e-learning, and video based e-learning group). The students of the e-learning group logged on the LMS in school computer lab and completed each e-learning. All students were required to take a pretest and posttest before and after learning under the direction of the subject teacher. A one-way analysis of covariance was administered to verify whether there was any difference between face-to-face lecture and e-learning in terms of students' learning outcomes after controlling the covariate variable, pretest score. According to the results, no differences between animation based and video based e-learning as well as between face-to-face learning and e-learning were identified. Findings suggest that the use of well designed e-learning could be worthy even in agricultural education, which stresses hands-on experience and lab activities if e-learning was used appropriately in combination with conventional learning. Further research is also suggested, focusing on a preference of e-learning content type and its relationship with learning outcome.