• Journal of Science Education
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• v.37 no.1
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• pp.117-130
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• 2013

The purpose of this study was to analyze the patterns and features of interaction in teacher interviewing with high school students' small group for biology learning. The interactions in variety between the students and between the students and the teacher were made as the interviews with each small group were repeated to feedback for biology learning. The patterns of interaction were categorized into four types by interactive level of interaction among group members and a teacher: leader representation without interaction among students and the teacher(LR, leader representation), interaction among a part of students and the teacher(PSI, partial students interaction), active interaction among students inside the group, but only interaction between the teacher and the leader student(SAI, students active interaction), and interaction between all of the students and the teacher(teacher-students active interaction). Even though complex patterns of interactions were made among the students at the initial stage of insufficient understanding on the study concept, the simple interaction processes were shown as students had gradually completed the understanding on the concept. It was displayed that the interaction in the small group for biology study provides the opportunity to confirm and understand the concept to the students who were poor at the understanding on the concept, and it can influence positively on the mutual creation of study concept.

• Women's Health Nursing
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• v.26 no.1
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• Knowledge Management Research
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• v.17 no.3
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• pp.207-225
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• 2016

Recently, flipped learning is being highlighted especially at the college of engineering because of its self-directed learning which direction matches that of the engineering education system. Although many engineering professors have tended to apply the flipped learning to their classes, they experienced a number of significant problems and challenges in practical implementing. By using TRIZ method as a tool for creative problem solving, that have been developed and applied in many fields ranging from engineering as well as management, this study aims to solve the education dilemma between learning efficiency and learning effectiveness, create a new concept of the flipped learning based on the solutions, and then explore its educational feasibility for successfully adopting it to engineering courses. This study suggests an instructional design for implementing the flipped learning according to the characteristics of engineering classes and then designs a new model of the flipped learning according to that procedure. Also, based on the empirical results in applying a new flipped learning model to the applied mathematics, this paper proposes several guidelines for the successful application of flipped learning in the future.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1894-1906
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• 2016

The purpose of this study is to analyze the setting and directions of cross-curricular learning topics based on research on experts' recognition of cross-curricular learning topics. The study method adopted was Delphi, and the subjects selected were curricular experts. This study has drawn following results: first, regarding the essence and problems of cross-curricular learning topics, even among the experts, there is no opinion agreed about cross-curricular learning topics' concept, essence, or characters. Second, more detailed discussion is demanded to select cross-curricular learning topics and set up a guideline about the operation. Third, it is needed to examine closely if presently suggested cross-curricular learning topics are duplicated or not and consider related subjects connected with those cross-curricular learning topics to improve education more systematically. Fourth, it is necessary to conduct more profound and systematic research on core competence that can embrace those cross-curricular learning topics. Fifth, to cope with changes in society and demands at school, it is needed to discuss how cross-curricular learning topics should be added or which learning topics should be added.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.375-383
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• 2020

In this study, we investigated the effects of the multiple representation-based learning strategies using augmented reality in terms of students' conceptual understanding, achievement, and enjoyment of science lessons. 136 8^th-grade students in a coed middle school were randomly assigned to the treatment and the control group. The students learned the concept of the particulate nature of matter related to the properties of substances for four class periods. The multiple representation-based learning strategies designed to facilitate the connecting and integrating representations provided from augmented reality were developed and administered to the students of the treatment group. Results of two-way ANCOVA revealed that the scores of a conceptions test and enjoyment of science lessons test of the treatment group were significantly higher than those of the control group, regardless of their prior science achievement. In a conceptions test, there was a significant difference in the concept of preservation of particles. However, the difference was not statistically significant in the concept of distribution and motion of particles. In terms of an achievement test, there was a significant interaction effect by their prior science achievement. The scores of low-level students were significantly improved, but the effects were not significant to high-level students. On the bases of the results, educational implications for effective teaching and learning using augmented reality are discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.27-30
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• 2006

Recently, the concept of ubiquitous learning which is regarded as so-called "beyond e-learning" has received remarkable interest along with the new social paradigm, namely, ubiquitous environment. The ubiquitous learning which is believed to lead educational renovation through information technology is recognized as the vision of future education. This paper intends to introduce the general ideas of ubiquitous learning and the present situation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.331-337
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• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions 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 overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.