• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.117-131
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• 2022

The purpose of this study is to develop a board game design activity for environmental education and to investigate how it affects high school students' environmental knowledge, environmental literacy, and creative engineering problem solving propensity. Twenty-five freshman and sophomore students belonging to the environmental club participated in this study. The board game activity was designed as 10 class hours lessons based on the engineering design process. The main data source of this study was pre-post students' conceptual maps of the environmental topics (global warming or ecosystem destruction) to measure the background knowledge on the topic. In addition, the environmental literacy measurement questionnaire and the creative engineering problem solving propensity questionnaire were collected before and after the activity. The result shows that the number of environment-related words described by the students on the post-concept map increased compared to the pre-concept map. In addition, the environmental literacy score and the creative engineering problem-solving propensity score increased significantly after performing the board game design activity. The results show that environmental lessons using board game design activities can help student develop various competencies in a good way. As above, research that combines board games and secondary education must be activated, and a new educational method and systemic support for in-service teachers who want to apply it to science classroom is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.201-203
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• 2011

We report an experimental result of applying a data mining algorithm for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). The preliminary empirical result of Naive Bayes classifier on HS-VOSTS questionnaire from one South Korean university students indicates that data mining algorithms can be effectively applied to automated knowledge discovery from students' survey data.

• Journal of the Korean earth science society
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• v.41 no.6
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• pp.599-616
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• 2020

Sea-surface temperature (SST) is one of the most important oceanic variables for understanding air-sea interactions, heat flux variations, and oceanic circulation in the global ocean. Extremely low SSTs from 0℃ down to -2℃ should be more important than other normal temperatures because of their notable roles in inducing and regulating global climate and environmental changes. To understand the temporal and spatial variability of such extremely low SSTs in the global ocean, the long-term SST climatology was calculated using the daily SST database of satellites observed for the period from 1982 to 2018. In addition, the locations of regions with extremely low surface temperatures of less than 0℃ and monthly variations of isothermal lines of 0℃ were investigated using World Ocean Atlas (WOA) climatology based on in-situ oceanic measurements. As a result, extremely low temperatures occupied considerable areas in polar regions such as the Arctic Ocean and Antarctic Ocean, and marginal seas at high latitudes. Six earth science textbooks were analyzed to investigate how these extremely low temperatures were visualized. In most textbooks, illustrations of SSTs began not from extremely low temperatures below 0℃ but from a relatively high temperature of 0℃ or higher, which prevented students from understanding of concepts and roles of the low SSTs. As data visualization is one of the key elements of data literacy, illustrations of the textbooks should be improved to ensure that SST data are adequately visualized in the textbooks. This study emphasized that oceanic literacy and data literacy could be cultivated and strengthened simultaneously through visualizations of oceanic big data by using satellite SST data and oceanic in-situ measurements.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.293-293
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• 2017

하천복원 또는 서식환경조성에 있어서 흐름영역을 통한 하천생태계 서식처 공간의 조성은 매우 중요하며 최적서식공간을 조성하기 위한 공간 확보 및 복원의 기술은 가장 중요하게 개발해야할 대상이다. 본 연구에서는 공간 확대에 따른 수환경 변화가 어류 물리서식처에 미치는 영향을 분석하기 위해 2차원 물리서식처 모형인 River2D를 적용하였다. River2D의 구동에 필요한 지형데이터는 만경강 유역의 봉동, 소양, 하리 3개의 지점을 경계지점으로 하여 대상영역 내 저지대 상습 침수구역인 만경강 본류와 소양천 합류지점을 복원대상지로 선정 후, 이 구역의 현재하도(공간복원전)와 합류부에서 제방을 없애 공간을 확보한(공간 복원후) 하도의 지형을 구축하였다. 그리고 과거 만경강 봉동일대에서 출현하였던 감돌고기를 대상어종으로 하여 감돌고기에 대한 생애주기별 서식처 적합도 지수(HSC)를 산정한 결과를 분석에 이용하였고, 유황조건은 IHA를 이용하여 산정된 5가지 유황조건을 경계조건으로 하여 분석을 실시하였다. 가중가용면적-유량관계곡선의 도출을 통해서 최적유량 산정시 중요한 지표인 WUA의 산정결과 현재 하천공간보다 공간 복원후 하천공간이 더 많은 가중가용면적을 확보하고 있음을 알 수 있고 동일한 유황의 조건에서 하천공간의 확보 및 복원을 통해 최소유량의 최대서식처 개선효과를 확인할 수 있었다. 또한 공간복원 후에 감돌고기에 대한 최대 가중가용면적은 20.4% 증가하였고, 최적유량변동은 20m3(57.1%) 저감하는 것을 확인할 수 있었다. 감돌고기의 생애주기별 복합서식처 적합도 분포결과는 복원후 전체 공간분포 중 소양천 합류부 및 복원 홍수터에서 서식처 적합도의 공간분포가 높게 나타났고 가중가용면적도 복원후 산란기(4~6월)에 57.7%, 성장기(7~8월)에 6.0%, 성어기(9~11월)에 41.9%로 가중가용 면적이 증가하였으며, 특히 산란기와 성어기의 가중가용면적이 하천공간 확보를 통해 크게 증가되었다. 이러한 결과들을 바탕으로 수생태계에서 하천어류의 서식환경을 보호하고 유지하기 위해서 적절한 서식공간의 확보가 선행되어야 하며 복원사업에 따른 제방제거 및 하상변동으로 발생하는 유속의 변화로 인한 다양한 영향을 충분히 검토하여야 할 것으로 판단된다.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.633-641
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• 2020

The development of intelligent information technology based on intelligence and data and network technology implemented by artificial intelligence has instigated innovation in society as a whole and has shown wide social and economic impact. Therefore, not only overseas but also in Korea, AI education is in a hurry to cultivate talents who will lead the upcoming society. Data is an important part of artificial intelligence, and data literacy, which can collect, process, and analyze data, to make data-based decisions, can be seen as an important competency to be developed along with AI literacy. Therefore, in this study, an AI data science education program that can increase data literacy of elementary school students was developed and applied to the experimental group, and its effectiveness was verified through a pre- and post response sample t-test. As a result, all of the four detailed competencies of data literacy, data understanding, collection, analysis, and expression, showed statistically significant improvement, indicating that the AI data science education program was effective in improving students' data literacy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.414-426
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• 2018

The purpose of this study is to conceptualize reading capital in the career context and to provide basic data for further study by exploring the components of reading capital. For this purpose, previous studies and literature were reviewed. In addition, we conducted big data analysis regarding 209 papers concerning various activities related to reading, and explored the components of reading capital. Through this study, reading capital can express personal, intangible ability such as literacy, experience, and attitude embodied through reading, and enable understanding persons, looking at the world positively, and creating personal, social and economic values. The components of reading capital are reading competency and humanistic knowledge; the former was conceptualized to be reading literacy, reading activity, reading attitude, reading ability, and the latter was conceptualized to be emotional intelligence, relationship, self-identity, creativity, adaptability, self-directedness and values. The definitions and components researched of the reading capital derived through this study are thought to be highly useful as basic data for the expansion of research between related studies.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1271-1276
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• 2006

DMB 데이터 표준화 위원회는 기존의 방송과 차별화 된 서비스를 제공하기 위하여 MPEG-4 BIFS Core 2D @ Level 1 을 지상파 DMB 데이터 표준으로 선택하였다. 그러나 MPEG-4 BIFS Core 2D @ Level 1 은 비디오 객체 위에 간단한 2D 도형을 오버레이하는 정도의 기능만을 제공하고 있어서, 아직은 DMB 데이터 표준에 3D 데이터 표현에 대한 내용을 포함하고 있지 않다. 따라서 본 논문에서는 향후 DMB 용 데이터 콘텐츠에서 3D 그래픽스를 활용한 멀티미디어 콘텐츠 개발이 가능하도록 3D 그래픽스 표현을 위한 프로파일을 제시하고자 한다. 방송의 새로운 영역인 모바일 환경에서의 BIFS 활용을 위하여 본 연구에서는 MPEG-4 BIFS 의 3D 노드들을 기반으로 모바일 환경에 적합한 노드들을 추려서 프로파일을 구성하였다. 이를 위해 콘텐츠 제작 시 노드 사용 빈도 및 각 노드별 리소스 사용 정도를 조사하였고, 모바일에서 그래픽스 객체 표현을 위한 언어인 OpenGL|ES, JSR184, Mobile Direct3D 등의 표준과 BIFS 표준을 비교 분석하였다. 이러한 조사 내용을 바탕으로 BIFS 3D 노드들 중에서 DMB 용 3D 콘텐츠 구현에 적합한 노드들을 분석하였으며 프로파일 검증을 위한 테스트 콘텐츠를 작성하였다. 향후 멀티미디어 콘텐츠는 다양한 3D 그래픽스 기술의 활용이 예상되므로 이와 같은 연구를 통해 DMB 환경에서도 3D 그래픽스를 객체를 표현이 가능하게 된다면, 다양한 데이터 콘텐츠 개발을 위한 발판이 되어 향후 데이터 방송 활성화에 기여할 수 있을 것이라 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.347-347
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• 2022

최근 데이터 사이언스의 비약적인 발전과 함께 다양한 형태의 딥러닝 알고리즘이 개발되어 수자원 분야에도 적용되고 있다. 이 연구에서는 LSTM(Long Short-Term Memory) 네트워크와 BO-LSTM이라는 베이지안 최적화(BO) 기술을 결합하여 일단위 앙상블 미래 댐유입량을 projection하는 딥 러닝 모델을 제안하였다. BO-LSTM 하이퍼파라미터 및 손실 함수는 베이지안 최적화 기법을 통해 훈련 및 최적화되며, BO 접근법은 모델의 하이퍼파라미터와 손실 함수를 높은 정확도로 빠르게 최적화할 수 있었다(R=0.92 및 NSE=0.85). 또한 미래 댐 유입량을 예측하기 위한 LSTM의 구조는 Forecasting 모형과 Proiection 모형으로 구분하여 두 모형의 장단점을 분석하였으며, 본 연구의 결과로부터 데이터 처리 단계가 모델 훈련의 효율성을 높이고 노이즈를 줄이는 데 효과적이고 미래 예측에 있어 LSTM 구조에 따른 영향을 확인할 수 있었다. 본 연구는 소양강 유역, 2020-2100년 기간 동안의 미래 예측에 적용되었다. 전반적으로, CIMIP6 데이터에 따르면 10%에서 50%의 미래 유입량 증가가 발생하는 것으로 확인되었으며, 이는 미래 강수량의 증가의 폭과 유사함을 확인하였다. 유입량 산정에 있어 신뢰할 수 있는 예측은 저수지 운영, 계획 및 관리에 있어 정책 입안자와 운영자에게 도움이 될 것입니다.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1005-1014
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• 2017

Focusing on the role of the science museum as an institution to improve the scientific literacy of the public, this study investigated public perception and behavior about science museum to know how much science museums affect the public by using social media big data analysis. For this purpose, we extracted texts containing 'science museum' in Naver blogs and Twitter, analyzed them by using network, frequency, co-ocurrence, and semantics analysis and compared them with the results in English speaking countries. As a result, blogs were mainly concerned with science museum among parents who have young children, while in Twitter posts from many students who visited as a group appeared. Therefore, the Korean public used science museum mainly as a space for children's experience, and in this case, programs and exhibitions of science museums are perceived positively. On the other hand, students who visited as a group showed some negative emotions. The result of comparison with the cases of foreign countries in terms of the function of the third generation science museum such as communications with the science museum and the public and the participation of the public in science, the Korean public hardly mentioned the scientific contents, words related to communications such as 'argue', and curators or staff after visiting the science museum. In contrast to many verbs related to meaningful activities such as 'learn', 'participate', 'listen', 'read', 'ask', 'think' appeared in English, only a small number of verbs include 'ask' and 'thin' appeared in Korean. Therefore, science museum need to improve impression, communicating with public, and involving activity with impact and variety after visit.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.