• Journal of Digital Convergence
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• v.9 no.6
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• pp.197-206
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• 2011

This study proposes the "Smart Learning" as a next generation for e-Learning, and focuses on developing policy for smart learning industry. Especially, this study's purpose is proposing a political agenda for smart learning industry as a knowledge-based industry. This study process for the realization of this purpose is belows: first, analyzing status of industry and policy in decades, second, finding problems and solutions, and finally, proposing the core subjects focused on practical value. And this study also focuses on the development of new policy suitable characteristics of the smart learning.

• Journal of Korean Biological Nursing Science
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• v.9 no.1
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• pp.5-31
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• 2007

Purpose: The purpose of this study was to explore the knowledge and learning needs on cardiac rehabilitation of coronary artery bypass graft(CABG) patients. Method: The subjects consisted of 100 CABG patients at A hospital in Seoul. Data were collected by the two different kind of questionnaires which measure knowledge and learning needs on cardiac rehabilitation of CABG patients. The subjects responded the questionnaire on knowledge before CABG and that on learning needs before their discharge. Result: The mean score of knowledge on cardiac rehabilitation was 68.54. Knowledge on risk factor, nature of disease, diet, daily activity, medication, post operative care were great in order. The mean score of learning needs on cardiac rehabilitation was 4.28. Learning needs on diet, medication, nature of disease, post operative care, daily activity, risk factor were great in order. There were significant differences in knowledge according to occupation, economic status and family history(p=.021, p=.017, p=.023). There was a positive correlation between knowledge and learning needs(r=.3009, p=.002). Conclusion: Level of knowledge on cardiac rehabilitation of CABG patients is low and knowledge on postoperative care is the lowest, and learning needs are great in ail categories.

• Journal of Korean Biological Nursing Science
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• v.20 no.2
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• pp.92-102
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• 2018

Purpose: The purpose of this study was to analyze the current status of studies related to the learning methods of biological nursing subjects in Korea. Methods: Five databases (KoreaMed, KMbase, NDSL, KISS, KiSTi) and grey literature were searched prior to February 2018. A total 12 studies met the inclusion criteria including 11 articles and 1 proceeding. Results: We included five experimental studies, five non-experimental studies, and two mixed method studies. First, most of the studies that applied a learning method focused on the subject of human anatomy and physiology; team-based learning was the method that was utilized the most. Second, the necessity of well-designed research was confirmed because the quality of included studies was low. Third, the research variables identified were mainly concentrated on the affective domain, and included satisfaction, motivation, self-efficacy, self-directed learning, confidence, attitude. We confirmed the need to develop a learning program that can also improve the cognitive and psychomotor domain variables in future research. Conclusion: The results of this study suggest that further research should be conducted with consideration the domain of research variables evenly. In addition, future studies should apply various learning methods and included randomized controlled trials.

• The Journal of Korean Association of Computer Education
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• v.5 no.1
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• pp.45-55
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• 2002

So far, there were a few web-based distance English learning systems for children. The existing systems for children have not appropriately reflected their learning characteristics and interests, since most of those systems regards mainly adults as targets. This thesis is aimed at proposing more effective educational service modules of web-based real-time system in order to encourage children's interests as much as possible in learning English. The web-based real-time distance learning system is a model based on the survey over the current use and satisfaction status of elementary school students who have actually used those kinds of systems. With the great expectation of more effective way of learning English, it also focuses on attracting parents to participate the learning process by providing a service to look over, in real-time, what their children do through the internet-based English learning system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• Korea Trade Review
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• v.46 no.6
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1208-1218
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• 2022

In the medical field, artificial intelligence has been used in various ways with many developments. However, most artificial intelligence technologies are developed so that one model can perform only one task, which is a limitation in designing the complex reading process of doctors with artificial intelligence. Multi-task learning is an optimal way to overcome the limitations of single-task learning methods. Multi-task learning can create a model that is efficient and advantageous for generalization by simultaneously integrating various tasks into one model. This study investigated the concepts, types, and similar concepts as multi-task learning, and examined the status and future possibilities of multi-task learning in the medical research.

• Journal of The Korean Association For Science Education
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• v.13 no.2
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• pp.146-151
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• 1993

This study analyzed the problem associated with inquiry centered science education and formulated some improvement Strategies for inquiry learning in the standard Korean high school course. In order to attain the goals of questionaire survey methods were used. To examine the current status of biology education, seperate questionaires were developed through an educational research and development procedure used for tearchers and student. The questionaires were developed to ask about instruction and evaluation methods, the level of inquiry learing and abstacles to it. Here are some of our results: 1) Biology instruction and learning is more knowledge-orinted than inquiry-orinted, 2) Inquiry approach in science teaching is hard to be applied because of crowed classroom conditions. 3) The material is too broad in range and too difficult in content. There is virtually nothing that can be related to everyday life. The material focusing on inquiry activities is unsatisfactorily selected and organized. 4) Effective methods of inquiry-based instruction and evaluation are not available. 5) Biology teachers are burdened with too many class hour a week and too many varieties of additional works. 6) 91.1% of biology teachers and 90.3% of students recognize that lab and field works are needed to enhance inquiry learning. However, in reality, such inquiry activities are lacking. 7) 73.3% of schools have no lab assistants. 8) The university entrance examination is the greatest factor against inquiry learning. 9) There are very few chances of in-service education for biology teachers to learn more about biology curriculum and science education theory.

• Journal of Korea Trade
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• v.25 no.5
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• pp.110-128
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• 2021

Purpose - The essential purpose of this study is to analyze the possibility of substitution of an individual job resulting from technological development represented by the 4th Industrial Resolution, considering the different effects of digital transformation on the labor market. Design/methodology - In order to estimate the substitution probability, this study used two data sets which the job characteristics data for individual occupations provided by KEIS and the information on occupational status of substitution provided by Frey and Osborne(2013). In total, 665 occupations were considered in this study. Of these, 80 occupations had data with labels of substitution status. The primary goal of estimation was to predict the degree of substitution for 607 of 665 occupations (excluding 58 with markers). It utilized three methods a principal component analysis, an unsupervised learning methodology of machine learning, and Ridge and Lasso from supervised learning methodology. After extracting significant variables based on the three methods, this study carried out logistics regression to estimate the probability of substitution for each occupation. Findings - The probability of substitution for other occupational groups did not significantly vary across individual models, and the rank order of the probabilities across occupational groups were similar across models. The mean of three methods of substitution probability was analyzed to be 45.3%. The highest value was obtained using the PCA method, and the lowest value was derived from the LASSO method. The average substitution probability of the trading industry was 45.1%, very similar to the overall average. Originality/value - This study has a significance in that it estimates the job substitution probability using various machine learning methods. The results of substitution probability estimation were compared by industry sector. In addition, This study attempts to compare between trade business and industry sector.

• Research in Mathematical Education
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• v.24 no.3
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• pp.131-136
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• 2021