• Journal of The Korean Association For Science Education
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• v.22 no.1
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• pp.93-101
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• 2002

This study investigated the effect of small group discussion for science concept learning in cooperative learning environments that encourage verbal interaction with peers upon students' understanding of the concepts at the particulate level, application abilities, perceptions of students' negotiation, and communication apprehension. Two classes of 7th grade at a coed middle school were assigned to the control and the treatment groups. They were taught about change of states and motion of molecules for 7 class periods. Two-way ANCOVA results revealed that the scores of a conceptions test for the treatment group were significantly higher than those for the control group, and that the low-achievers in the treatment group performed better than those in the control group. However, the scores of two groups did not differ significantly in an application test. The perceptions of students' negotiation for the treatment group were more positive, but the scores of the communication apprehension for two groups were not significantly different.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.4
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• pp.85-104
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• 2004

This study is supposed to the way to add and enlarge the elements related to educational domain in metadata of school library information system (DLS) by using the concept of learning object which the education information service agencies have adapted. This study is to propose the methods which can be accessed according to the units of learning content in order that they can be applied to the teaching and learning situations, and describe and index the total traits of interior data elements included in the information resources. Thus, the metadata of the existing DLS through the additional elements : , , and was made to access the information resources according to the teaching and learning situations and to accept the concept of interior learning units by means of the element.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.241-248
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• 2015

Nursing clinical practice, especially because it is required to reproduce this fusion education is very urgent. This Study was done to examine the effect of action learning techniques in simulation class. The study was designed using a nonequivalent control group pretest-posttest design. The participants consisted of control group 92, experimental group 92. The data analyzed using SPSS 18.0 program. Professional self-concept are higher than in the control group were measured.(t=-5.118, p=>.001). communication competence and self-directed learning capability of experimental group increased significantly from those control group. This result means that can help to significantly improve the professional nursing students learning techniques to simulate the application of an action class. In other words, if the act of creative training techniques such as future action learning hands-on training to be a big help.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• Health Policy and Management
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• v.18 no.4
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• pp.111-124
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• 2008

New ways of work-shift and learning programs, which were based on the concept of 'performance improvement through people', have been introduced to healthcare organizations. I analyzed the performance of the changes and the performance differences. Data were collected through interview and survey. I discussed that modification of management practices which were developed in manufacturing organizations is important for successful implementation in healthcare organizations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2424-2429
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• 2003

The importance of e-Learning, which supports to study anywhere and anytime, has been pointed out for improving education. There have been various research papers on e-Learning system for educations. Most of literatures have focused on guiding the student or measuring understanding their level etc. Design method of e-Learning system has not been discussed based on structure and analysis of the class. In this paper, scheme of the class is proposed by analyzing and structuring class, then design method of e-Learning system is discussed based on it.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.308-313
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• 1998

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general description of concepts from specific instances of these concepts. In many real life situations, however, new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overcall set of instances. The method of learning presented here is base don a rough set concept proposed by Pawlak[2][11]. It is shown an algorithm to find minimal set of rules using reduct change theorems giving criteria for minimum recalculation with an illustrative example. Finally, the proposed learning algorithm is applied to fuzzy system to learn sampled I/O data.

• Hwankyungkyoyuk
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• v.23 no.4
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• pp.15-31
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• 2010

This research aimed for revealing social interaction-based environmental learning elements at the natural history museum. First, we carried out a survey of the perceptual change about 'the environment' concept. It was for confirming whether environmental learning occurred in visiting. Second, we found environmental learning elements through analyzing dialogues among small group members during looking around the museum. Social interaction-based environmental learning elements were classified into five; knowledge, affection, value, skill, behaviour. Each environmental learning element included several sub-elements, so we revealed totally 9 environmental learning sub-elements. By results, we said characteristics and possibility of the natural history museum as an informal environmental educational institution. It is meaningful for extending areas of environmental education.