• Journal of the Korean Society of Earth Science Education
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• v.5 no.1
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• pp.114-123
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• 2012

This study investigates the effect of environmental club activities of the creative experiential learning activities on the improvement of students' Environmental Literacy and Pro-environmental Behavior. For that purpose, This study divided 24 students at Jinhae U Elementary School into test groups of 12 students and Control groups of 12 students. Test groups participated in the 'GomGomi' environmental Club for sixteen times from March 2011 to December 2011. Conclusions of this study include; First, we found that the environmental club activities is effective to improve the students' environmental Literacy overall. Specifically, the Environmental Club Activities was effective in the sub-catagories of environmental Literacy such as 'rights of nature' and 'eco-crisis'. However, there was no significant change in the sub-category of 'human exemptionalism' Second, we found that the environmental club activities is effective to improve students' pro-environmental behavior overall. Specifically, the environmental club activities was effective in the sub-domains of Pro-environmental Behavior such as 'cognitive domain' and 'affective domain'. However, there was no significant change in the sub-domains of 'behavioral domain'. Summary, the environmental club activities was shown to be effective for improving their environmental literacy and pro-environmental behavior; This study implies that the environmental club activities of the creative experiential learning activities would be a effective tool to help students to improve their environmental literacy and pro-environmental behavior.

• The Journal of Korean Academic Society of Nursing Education
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• v.1 no.1
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• pp.46-61
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• 1995

The purpose of this study was to identify the degree of learning desire in the health education of military drill curriculum and to furnish basic data for the effective health education at high school. A total of 594 high school students were participated in the questionnaires and the 12 domains & the 55 questions were utilized for this study. The survey was conducted from March 15 to March 27, 1993 and the collected date were analized by T-test and F-test. The results of this study are as followings : 1. In the 12 domains, respondents indicated high degree of learning desire in 'human & sex', 'nutrition' and 'disease of adult' in that order. However 'accident & disaster', 'transport & management' and 'nursing' were not highly ranked. 2. In the 55 questions, respondents showed high degree of learning desire in 'artificial respiration' & 'cardiopulmonary resuscitation', 'precautionary of adult disease' and 'sex & sex moral' in that order, but low degree of learning desire in 'the management of many wounded persons' and the 'synopsis of nursing'. 3. Comparing the degree of learning desire by grade, the 1st, the 3rd and the End grade were ranked in that order. The 1st and 3rd graders showed higher degree of learning desire in 'human & sex', and the 2nd graders in 'nutrition'. 4. Also, female students showed higher degree of learning desire than male students in general. Female students indicated it in 'nutrition', 'human & sex' and 'adult disease' in that order while male students in 'human & sex', 'adult disease' and 'nutrition' in that order. 5. The academic high school students showed higher degree of learning desire than the vocational high school students. 'Human & sex' was highest ranked at both academic and vocational high school students.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4835-4848
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• 2020

The recent expansion of the digital content market is increasing the technical demand for various facial image transformations within the virtual environment. The recent image translation technology enables changes between various domains. However, current image-to-image translation techniques do not provide stable performance through unsupervised learning, especially for shape learning in the face transition field. This is because the face is a highly sensitive feature, and the quality of the resulting image is significantly affected, especially if the transitions in the eyes, nose, and mouth are not effectively performed. We herein propose a new unsupervised method that can transform an in-wild face image into another face style through radical transformation. Specifically, the proposed method applies two face-specific feature loss functions for a generative adversarial network. The proposed technique shows that stable domain conversion to other domains is possible while maintaining the image characteristics in the eyes, nose, and mouth.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.9 no.1
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• pp.7-15
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• 2002

Purpose: To analyze the learning objectives and test items for Fundamentals in Nursing which were established between 1999 and 2000 and to develop these items toward a nationwide faculty workshop for national board examination for Korean nurses Results: 1. According to Bloom's taxonomy, learning objectives established in 1999 mainly consisted of two domains, comprehension (56 1%) and knowledge (27.7%). The learning objectives established in 2000, mainly consisted of comprehension (45.2%) and application (25.4%). 2. According to McGuire's taxonomy, domain of test items established in 1999 consisted of recall (66.7%). interpretation (28.0%) and problem solving (4.9%). Domain of test items established in 2000, consisted of recall (65.1%), interpretation (22.0%) and problem solving (12.9%). 3. The proportion of learning objectives in the knowledge and comprehension domains established in 200, decreased from 27.7% to 13.5%, and from 56.1% to 45.2% respectively over that of 1999. But the domain of application Increased from 5.3% to 25.4% over that of 1999. 4. With regard to McGuire's taxonomy, the proportion for the recall and interpretation domains established in 2000 decreased from 66 7% to 65.1%, and from 28.0% to 22.0% respectively. But the proportion for the problem solving domain increased from 4.9% to 12.9% over that of 1999. For type of test items, the proportion of A type established in 2000 decreased from 47.2% to 37 6%, and K type increased from 52.1% to 60.8% over that of 1999. Conclusion: The learning objectives and test items established in 2000 showed remarkable improvement compared to those established in 1999. For better learning objectives and test items in Fundamentals of Nursing, further research is recommended on essential content and standardization of job analysis for national board examination for nurses in Korea.

• International journal of advanced smart convergence
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• v.3 no.1
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• pp.11-14
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• 2014

We have been designing human interactions for some learning support system or education system. The design is based on a symbol grounding model. The model is applicable to many learning domains using virtual reality technology. The design policy is simple and compact. In order to realize the policy we use/reuse some devices from the viewpoint of virtual reality. This paper introduces basic ideas and explains several example cases based on the idea.

• Hwankyungkyoyuk
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• v.12 no.1
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• pp.19-39
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• 1999

Firstly, the goals and the domains of contents of environmental education was classified in order to systematize the contents of environmental education which would be taught in each subject. According to these goals and domains of contents, the contents of 10 subjects (Korean Language, Ethics, Social Studies, Mathematics, Science, Music, Arts, Physical Practicum(Technology and Heme Economics), English were analyzed. The norms in the analysis of the goals of environmental education by each subject were 4 domains: information and knowledge, skills, value & attitudes, & action and participation. The norms in the analysis of the contents of environmental education by each subject were 11 domains: natural environment, artificial environment, population, industrialization/urbanization, resources, environmental pollution, environmental preservation and measures, environmental sanitation, environmental ethics, environmentally sound and sustainable development(ESSD), and sound consumption life. As a result, it was found that all the 4 domains of goals in environmental education could come true. Furthermore, the goals of environmental education were found to be reached in the subjects of Korean Language, Music, Arts, Physical Education, Mathematics, English, etc., which had been thought to have nothing to do with environmental education. It was also found that the contents of each subject could deal with its own unique environmental contents. The result of this study can keep all subjects from overlapping in environmental contents, and can make the most of each subject's characteristics. Also, the result of this study will be referenced in developing the teaching and learning materials for environmental education according to each subject.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• East Asian mathematical journal
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• v.37 no.2
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• pp.197-221
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• 2021

In this study, a men-tutoring model was applied to four second-year high school students in mathematics learning counseling that could cultivate cognitive and affective domains. By observing and analyzing the cases, we examined the effects of mathematics learning counseling on students' mathematical attitudes and self-regulated learning ability. The results of mentoring applied to mathematics learning counseling had a positive effect on reinforcing prerequisite learning, improving self-regulated learning ability, strengthening mathematical strategies, and inducing learning motivation and maintaining interest. We are looking forward to that men-tutoring can be used effectively for students who have difficulty learning mathematics.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.292-297
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• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.