• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.409-423
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• 2008

Many researches have reported that it is difficult to solve students' difficulties in learning science with teaching models focused on certain aspects because of various reasons. Recently, in science education research, the integrated perceptive has been to put emphasis on understanding complex situations of real teaching and learning. In this research context, the integrated mental model theory that were considered as a whole factor related to learning has been studied by integrating previous studies that related to students' conceptions and learning in various fields. Thus, it is needed that the teaching model be based on the integrated mental model theory to help students to solve their difficulties. The purpose of this research was to develop a new teaching model based on the integrated mental model theory to address this issue. We reviewed current studies on student difficulties and teaching models. After this, we developed 4M learning cycle teaching model. In this paper, we described the process of developing a new teaching model and discussed how to apply this teaching model to the practices. We also discussed the effects of 4M learning cycle teaching model based on the integrated mental model theory in learning science with its implications.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.22 no.8
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• pp.50-56
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• 2004

인터넷 제반기반 및 관련 서비스의 발전은 물리적인 시간과 공간의 한계를 뛰어 넘어 언제, 어디서나 학습할 수 있는 e-learning의 기초가 되었다. ［1］에서는 e-learning을 ‘장소나 시간의 제약 없이 높은 수준의 양방향 교육을 제공하기 위한 서비스와 기술’이라 정의하였다. 우리는 e-learning을 학습자와 시스템간의 양방향 커뮤니케이션(communication)을 이용한 새로운 학습 활동으로 정의하고, e-learning 시스템을 멀티미디어 (multimedia)로 이루어진 학습 콘텐츠(contents)를 통신망을 이용하여 학습자에게 전달하고, 학습자 정보를 전달받아 학습효과를 높을 수 있는 정보로서 재가공하는 역할을 담당하는 소프트웨어(software) 및 하드웨어(hardware)로 정의한다. e-learning은 책상과 칠판으로 연상 될 수 있는 현재의 강의 방식을 단순하게 디지털화(digitize)하는 수준을 넘어 효과적이고 새로운 교육방식을 지칭하고 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.273-276
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• 2020

기존의 물리 센서를 활용한 주차 감지는 주차장 규모가 클수록 큰 비용이 필요하고 이미지 기반의 분석은 개별 주차장에 대한 데이터 라벨링과 학습의 노력이 필요했다. 본 논문은 LoRa(Long Range) 네트워크와 마이크로프로세서를 활용한 IoT기반의 시스템으로 영상데이터를 서버로 전송하고 COCO(Common Object in context) 데이터셋으로 학습된 Mask R-CNN 기반의 모델을 활용한 주차장 내 차량점유 감지 알고리즘을 통해 개별 주차장에 대한 학습 또는 라벨링 없이 주차장 내 주차상태를 식별하고 사용자에게 인터페이스를 통해 실시간으로 주차정보를 제공하는 시스템을 구현한다.

• Journal of Korean Elementary Science Education
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• v.25 no.4
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• pp.383-395
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• 2006

School science is often criticized as being too remote from both learners' interests and needs and as maintaining scientist-oriented approaches rather than humanistic ones. Although science is mainly taught on the basis of textbooks inside classrooms, the learning of science can not be confined to the boundaries of curriculum and school. Firstly, this paper briefly reviews and characterizes the historical development of science education with a series of analogies, and then suggests a new analogy, a so-called 'Hearts-On' approach to science education which emphasizes the humanistic aspects and the contextual dimension of science education. Secondly, it critically examines how much traditional school science teaching, particularly in physics, is limited in terms of the context of learning (i.e. textbook, laboratory, classroom, local, and global) as well as in terms of the context of the contents (i.e. physical, personal, social, and global). Thirdly, some recent attempts initiated by the author and colleagues are explained as examples of the Hearts-On approach to science education. In particular, a series of community-based science programs led by SNU and the development of a series of books on 'Contextual Physics'(i.e. Body Physics, Wearing Physics, Dining Table Physics, and Sports Physics) are outlined. Finally, the idea of scientific humanism is explored in relation to the context-rich approaches in science education. It is hoped that this paper helps us to reconsider how we can expand the world of science education beyond the boundaries of the curriculum and school and into a more humanistic one.

• Geophysics and Geophysical Exploration
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• v.27 no.3
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• pp.171-180
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• 2024

The processing of seismic data involves analyzing earthquake wave data to understand the internal structure and characteristics of the Earth, which requires high computational power. Recently, machine learning (ML) techniques have been introduced to address these challenges and have been utilized in various tasks such as noise reduction and velocity model construction. However, most studies have focused on specific seismic data processing tasks, limiting the full utilization of similar features and structures inherent in the datasets. In this study, we compared the efficacy of using receiver-wise time-series data ("receiver array") and synchronized receiver signals ("time array") from shotgathers for pretraining a Bidirectional Encoder Representations from Transformers (BERT) model. To this end, shotgather data generated from a synthetic model containing faults was used to perform noise reduction, velocity prediction, and fault detection tasks. In the task of random noise reduction, both the receiver and time arrays showed good performance. However, for tasks requiring the identification of spatial distributions, such as velocity estimation and fault detection, the results from the time array were superior.

• Journal of the Korean earth science society
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• v.35 no.6
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• pp.484-497
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• 2014

This study aimed to investigate high school students' conceptions of plate tectonics. Seventy students (N=70) in their 2 year of high schools in the province of Chungcheong-do participated in this study, and they took a conception test of visual representation. We conducted a semi-structured interview with 10 volunteering participants out of seventy. After learning about the concept of plate tectonics, this study found that participants ended up having alternative conceptions relating to terminology, meaning of colors, plates' movement, plates' boundaries, position and cause of melting, mantle's physical conditions, and driving forces of plate motion. The conceptions that the participants held after class were organically related, which included the meaning of colors, mantle's physical characteristic, and driving forces of plate movement. In addition, the visual representation used for teaching plate tectonics influenced the students' understanding about terminologies, plates' boundaries, position of melting, and the physical characteristic of mantle. The study found that there were the factors of visual representation that caused the learners to create alternative conceptions in learning about plate tectonics.

• Journal of the Korean Physical Therapy Association
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• v.10 no.2
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• pp.47-58
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• 1989

• Journal of The Korean Association For Science Education
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• v.20 no.4
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• pp.491-504
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• 2000

Recent research has shown that students' interest in learning physics is not a simple one. In this study the dimensions of students' interest in learning physics have been identified. These are the topic being taught, the activity in which the students are involved, and the motive of the students for learning physics. A Likert-style pilot questionnaire was constructed for identifying these dimensions and given to 13 year-old 162 students. A factor analysis of the results indicates that there are meaningful sub-dimensions in interest. In other words, while there were no specific sub-dimensions in topic dimension, motive dimension could be divided into intrinsic motive and extrinsic motive, and activity dimension could also be divided into receptive, experiential, high cognitive, and interactive activity.

• Journal of The Korean Association of Information Education
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• v.3 no.1
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• pp.13-21
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• 1999

'Interactivity' is considered as a key factor of user-centered interactive instructional systems design in Web environments. To provide an optimal level of interactivity for learners is an ideal goal in instructional systems design(ISD) process. Based on the primary concern of interactive systems design which pursues how the power of interactivity can be utilized and put to work, several interactive design strategies in Web-based systems are recommended through this research.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1605-1609
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• 2010

CGCM3.1 SRES B1 시나리오의 2D 변수들을 입력값으로 인공신경망 모형을 이용한 스케일 상세화기법으로 강부식(2009)은 소양강댐 유역의 월 누적강수 경향분석을 실시하였다. 원시 GCM 시나리오를 스케일 상세화 시키기 위한 기법의 하나로 인공신경망 모형을 사용할 수 있는데, 이 경우 GCM에서 모의되는 강수플럭스, 해면기압, 지표면 근처에서의 일 평균온도, 지표면 근처에서의 일평균온도, 지표면으로부터 발생하는 잠열플럭스 등과 같은 22개의 변수를 잠재적인 예측인자로 사용하여 신경망을 구성하게 된다. 입력변수세트의 구성은 인공신경망의 계산 효율을 좌우하는 중요한 요소라 할 수 있다. 본 연구에서는 변수의 물리적 특성을 고려하여 순차적인 변수선택을 통한 신경망 입력변수 세트를 구성하고 입력세트 간의 학습성과 비교를 통하여, 최적 입력변수 선정 및 신경망의 학습효과를 높일 수 있는 방법에 대해 연구하였다. 물리적 상관성이 높다고 판단되는 GCM_Prec, huss, ps를 입력변수로 하여 순차적인 케이스를 학습해본 결과 huss와 ps를 입력변수로 하는 케이스에 대해서 적은 오차와 높은 상관성을 보였다, 또한, 신경망의 학습 효과를 높이기 위해 홍수기와 비홍수기로 구분하여 학습한 결과 홍수기와 비홍수기로 구분하여 신경망을 구성하였을 경우가 향상된 모의값을 나타내었다. 기후변화모의자료는 CCCma(Canadian Center for Climate Modeling and Analysis)에서 제공되는 CGCM3.1/T63 20C3M 시나리오를 사용하였으며, 관측값으로는 AWS에서 제공된 일 누적강수를 사용하였다. 인공신경망의 학습기간은 1997년부터 2000년이며, 검증기간은 2001년부터 2004년으로 구성하였다.