• Journal of The Korean Association For Science Education
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• v.33 no.5
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• pp.1021-1040
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• 2013

In order to explore the influences of coteaching through mentoring upon the teaching professionalism of beginning science-gifted education teachers, this case study deeply investigated the change processes in the aspects of pedagogical content knowledge (PCK). Two beginning teachers planned, performed and reflected together their science instructions for science-gifted students in secondary school during four 3-hour classes. Since the second instruction, pre-, during-, and post-mentoring were conducted, we collected various data related to teachers' planning processes, videotaped all coteaching science classes, and wrote field notes. We also recorded in-depth interviews with the teachers and the whole process of mentoring. All the data were analyzed by using the constant comparative method. The results of the analyses indicated that coteaching through mentoring positively changed the teachers' PCK. Above all, we found that coteaching and mentoring strategies generated a significant synergy effect through a mutually complementary relationship. The teachers developed deep practical knowledge about the enrichment curriculum, which placed more emphasis on developing cognitive and/or affective characteristics of science-gifted students. The teachers also improved their knowledge about the characteristics of science-gifted students and the instructional strategies appropriate for developing them. Moreover, practical knowledge about assessment domains and methods used in science-gifted education were improved. Knowledge on science content necessary for effective inquiry instruction was also improved.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.81-94
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• 2024

This study explores and conducts an in-depth comparison of the epistemic characteristics in different data types utilized in the science inquiries of preservice teachers regarding noise as a risk in everyday life. Focusing on primary and secondary data in the context of science inquiries about noise, we examined how these data types differ in science inquires in terms of inquiry design, data collection, and analyses. The findings reveal that sensor-based primary data enable direct measurement and observation of key phenomena. Conversely, secondary data rely on predetermined measurement methods within a public data system. These differences require different epistemic considerations during the inquiry process. Based on these findings, we discuss the educational implications concerning teaching approaches for science inquiries, teacher education for inquiry teaching, and the development of risk response competencies in preparation for the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) era.

• Journal of The Korean Association For Science Education
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• v.31 no.1
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• pp.78-98
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• 2011

The purpose of this study was to analyze elementary school students' interpretation of data characteristics by cognitive style. Participants were elementary students in sixth grade who can use integrated inquiry process skills. The students were divided into two groups, analytic cognitive style and wholistic cognitive style according to their response to Cognitive Style Analysis. They performed scientific interpretation of data activity. To collect data for this study, participants recorded the result on scientific interpretation of data activity paper and researcher recorded the situation on videotape and interviewed with participants after the end of interpretation of data to get additional data. And the findings of this study were as follows: First, the study analyzed interpretation of data characteristics by the operator regarding different situations of interpreting data according to cognitive style. For example, in the intermediate state, analytic-cognitive style students showed high achievement in identifying variables, and wholistic-cognitive style students were active in using prior knowledge to interpret data. Second, the result of analysis on the direction of interpreting data and preference for data types in interpreting data activities according to cognitive style are as follows: Wholistic-cognitive style students showed relatively high perception of information through the top-down approach. On the other hand, analytic-cognitive style students usually used the bottom-up approach gradually expanding detailed information to the scientific question-related answer and showed a preference data of the table type. Through the result, this study aimed to help establish a data interpretation strategy for learners to solve problems based on understanding of interpretation of data characteristics according to learners' cognitive style, and purposed the instruction design suggesting the data requiring various data interpretation strategies to develop learners' data interpretation ability.

• Journal of the Korean Chemical Society
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• v.60 no.5
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• pp.342-352
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• 2016

The purpose of this study was to investigate inquiry-based science instruction developed and implemented by a pre-service chemistry teacher regarding the difficulties that she encountered and the ways how she tried to solve out problems. Main data of this study were pre-service teacher reflections that were written after developing both each lesson plan and the whole 10 lesson plans, and after implementing both each lesson and the whole classes. Supplemental data were lesson plans, class audio recordings, and student written journals. The pre-service teacher learned that she was lack of science content knowledge and understanding of students’ understandings. Also she had difficulties of developing inquiry-based science lesson plans, managing classrooms, and guiding students to engage in science inquiry. In order to overcome the difficulties, she asked for advice to experienced teachers, studied science concepts using textbooks and internet resources, provided detailed and concrete guidance for student argumentation and writing.

• Journal of The Korean Association For Science Education
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• v.30 no.2
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• pp.291-303
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• 2010

The purpose of this study is to investigate the difficulties in engaging in open-inquiry activities of elementary pre-service teachers. Data were collected from in-depth interviews with 22 pre-service teachers and their open-inquiry products. The 22 pre-service teachers who participated in this study were enrolled in college openinquiry course and performed open-inquiry activities for fifteen weeks. The results of this study show that pre-service teachers struggled with several difficulties; selection of the subject, the lack of understanding about the science process skills and content knowledge, background knowledge, environment matters and self confidence about open inquiry. However, they thought open-inquiry is interesting in spite of much difficulties, and is necessary for science education.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.

• Journal of The Korean Association For Science Education
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• v.8 no.1
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• pp.81-94
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• 1988

본 연구의 목적은 Piaget의 인지발달단계(전조작 구체적 조작 단계)에 따른 자연과 개념획득에 효과적인 교수전략을 탐색하기 위한 연구로서, 첫째는 문헌연구를 통해서, Piaget의 인지이론을 기초로한 교육론과 개념학습을 위한 교수전략을 연구함으로써 전조작 단계의 어린이, 구체적 조작단계의 어린이에게는 연역적 교수전략보다 귀납적 교수전략이 더 효과적이며, 구체적 조작단계의 어린이에게는 초보적인 논리능력이 발달하므로 연역적 교수전략도 가능하며, 이 2가지의 인지단계가 복합돼 있는 국민학교 2학년 집단에게는 귀납적 교수전략이 보다 효과적일 것이라는 데에 이르렀다. 둘째로는 현장 실험연구로서 실제로 자연과 2학년의 한 단원을 연억 귀납의 교수전략의 단계에 맞추어 적용하였다. Piaget의 인지 발달 단계예 따른 자연과 개념획득을 위한 효과적인 교수전략을 탐색해본 결과는 다음과 같다. 첫째, 전조작 단계의 아동은 자연과 개념획득을 위해서는 연역적 교수전략보다 귀납적 교수전략이 더 효과적이다. 둘째, 구체적 조작단계의 아동에게는자연과 개념획득을 위한 교수전략은 귀납적 교수전략이 효과적이지만, 연역적 교수전략도 가능하다는 것이다. 이로써 국민학교 2학년 즉, 전조작 단계와 구체척 조작단계가 복합돼있는 집단에게는 연역적 교수전략보다 귀납적 교수전략이 보다 더 효과적이라는 것이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.84-86
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• 2018

요즘 자율주행과 같은 최신 기술의 발전과 더불어 촬영된 영상 장면에 대한 깊이있는 이해가 필요하게 되었다. 특히, 기계학습 기술이 발전하면서 카메라로 찍은 영상에 대한 의미론적 분할 기술에 대한 연구도 활발히 진행되고 있다. FuseNet은 인코더-디코더 구조를 이용하여 장면 내에 있는 객체에 대한 의미론적 분할 기술을 적용할 수 있는 신경망 모델이다. FuseNet은 오직 RGB 입력을 받는 기존의 FCN보다 깊이정보까지 활용하여 RGB 정보를 기반으로 추출한 특징지도와의 요소합 연산을 통해 멀티 모달 구조를 구현했다. 의미론적 분할 연구에서는 객체의 전역 컨텍스트가 고려되는 것이 중요한데, 이를 위해 여러 계층을 깊게 쌓으면 연산량이 많아지는 단점이 있다. 이를 극복하기 위해서 기존의 합성곱 방식을 벗어나 새롭게 제안된 팽창 합성곱 연산(Dilated Convolution)을 이용하면 객체의 수용 영역이 효과적으로 넓어지고 연산량이 적어질 수 있다. 본 논문에서는 컨볼루션 연산의 새로운 방법론적 접근 중 하나인 팽창된 합성곱 연산을 이용해 의미론적 분할 연구에서 새로운 멀티 모달 네트워크의 성능 향상 방법을 적용하여 계층을 더 깊게 쌓지 않더라도 파라미터의 증가 없이 해상도를 유지하면서 네트워크의 전체 성능을 향상할 수 있는 최적화된 방법을 제안한다.

• Journal of architectural history
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• v.31 no.1
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• pp.41-50
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• 2022

Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.