• 과학교육연구지
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• 제37권1호
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• pp.117-130
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• 2013

본 연구는 고등학생들의 생물 학습 상황의 소집단 활동에서 나타나는 상호작용 유형을 분석하고자 하였다. 상호작용의 유형 분석은 소집단 활동을 위해 교사가 피드백을 제공하고자 하는 면담 과정을 통해 이루어졌다. 상호작용의 유형은 학생과 학생, 학생과 교사간 상호작용 수준에 따라 4가지로 분류되었는데 이는 소집단 내에서 교사와 소집단의 대표 학생 사이에서만 상호작용이 이루어지는 유형(LR, Leader Representation), 일부 학생과 교사의 상호작용이 이루어지고 있는 유형(PSI, Partial Students Interaction), 학생과 학생 사이에 상호작용이 활발히 일어나나 교사와는 대표 학생과 상호작용이 이루어지는 유형(SAI, Students Active Interaction), 구성원 모두가 활발히 상호작용을 하고 모든 학생들이 교사와도 상호작용을 하는 유형(TSAI, Teacher-students Active Interaction) 등이다. 고등학생들은 면담 과정이 거듭될수록 학생과 학생 사이에 상호작용이 활발하게 일어났으며, 학습에 대한 개념 이해가 부족한 초기 단계에서는 학생들 간에 복잡한 상호작용 양상이 나타나지만 개념의 이해가 완성되어 갈수록 점차 상호작용 유형이 간결하게 변화되어가는 특성을 보였다. 이로부터 생물 학습을 위한 소집단에서의 상호작용은 개념 이해가 부족한 학습자에게는 개념을 확인하고 이해할 수 있는 기회를 제공하며, 학습 개념을 상호 형성할 수 있는 긍정적 영향을 미칠 수 있을 것이다.

• 여성건강간호학회지
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• 제26권1호
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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.

• 지식경영연구
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• 제17권3호
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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.

• 수산해양교육연구
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• 제28권6호
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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.

• 한국과학교육학회지
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• 제40권4호
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• pp.375-383
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• 2020

이 연구에서는 증강현실을 활용한 다중 표상 학습 전략이 학생들의 개념 이해도, 학업 성취도, 과학 수업에 대한 즐거움에 미치는 영향을 조사하였다. 남녀 공학 중학교 2학년 학생 136명을 처치 집단과 통제집단으로 무선 배치하였다. 학생들은 네 차시 동안 물질의 특성과 관련한 입자 개념을 학습하였다. 이때 처치 집단의 학생들에게는 증강현실이 제공하는 표상들 사이의 연계와 통합을 촉진할 수 있는 다중 표상 학습 전략을 개발하여 적용하였다. 이원 공변량 분석 결과, 개념 이해도, 과학 수업에 대한 즐거움 검사에 대한 처치 집단의 점수는 학생들의 사전 성취 수준과 무관하게 통제 집단보다 유의미하게 높았다. 개념 이해도 검사의 하위 개념 중 입자의 보존에 대해서는 유의미한 차이가 나타났으나, 분포 및 운동에 대해서는 유의미한 차이가 나타나지 않았다. 학업 성취도 측면에서는 사전 성취 수준과 유의미한 상호작용 효과가 나타났다. 이때 하위권 학생들의 성취도는 유의미하게 향상되었으나 상위권 학생들에게는 유의미한 효과가 나타나지 않았다. 연구 결과를 바탕으로 과학 교수학습에서 증강현실의 효과적인 활용을 위한 교육적 시사점을 논의하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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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.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 춘계 종합학술대회 논문집
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• pp.27-30
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• 2006

최근 들어 새로운 사회 패러다임으로 등장하고 있는 유비쿼터스 환경에 발맞추어 교육 분야에서도 beyond e-learning이라는 유비쿼터스 러닝이라는 개념이 대두되고 있다. IT를 통한 교육의 혁신을 초래하게 되는 유비쿼터스 교육은 미래 교육의 비전으로 인정되고 있다. 본 연구에서는 유비쿼터스 시대의 교육인 유비쿼터스 교육에 대한 개념을 정리하고 현재 유비쿼터스 교육에 대한 동향을 소개하고자 한다. 또한 국내 유비쿼터스 교육의 운영사례를 통하여 시사점을 알아보고자 한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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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.

• 한국멀티미디어학회논문지
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• 제20권11호
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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.