• Journal of The Korean Association For Science Education
• /
• v.40 no.3
• /
• pp.237-251
• /
• 2020

Issues on climate change we are facing, such as global warming, are very important as it affects our lives directly. To overcome this, efforts to reduce greenhouse gases emissions (e.g., carbon dioxide) are necessary and these efforts should be based on our integrated understanding of carbon cycle. The purpose of this study is to examine the research trend on carbon cycle education and to suggest the value and direction of carbon cycle education for students who will be citizens of the future. We analyzed 52 carbon cycle education related studies collected from academic research databases (RISS, KCI, ERIC, Google Scholar, and others). As a result, we conclude that resources are still limited and more researches on verification and utilization of developed program, development of accurate and comprehensive tools for students' recognition and level assessment, developing educational model or teacher professional development, providing more appropriate curriculum resources, and the use of various topics or materials for carbon cycle education are necessary. Students' comprehensive understanding of the carbon cycle is important to actively react to the changes in the global environment. Therefore, to support such learning opportunities, resources that can be connected to students' daily experiences to improve students' understanding of carbon cycle and replace misconceptions based on the verification of existing programs should be provided in the classroom as well as the curriculum. In addition, sufficient exemplary cases in carbon cycle education including various materials and topics should be provided through professional development to support teachers teaching strategies with carbon cycle.

• Journal of The Korean Association of Information Education
• /
• v.16 no.4
• /
• pp.383-395
• /
• 2012

This study investigated the recognition of teachers on the introduction and use of SMART education, which is an ICT-based customized learning method. Through the investigation, the study was to empirically examine the relationship between the use intention of SMART education and its influential factors, and analyze the difference in the use intention of SMART education by teachers, focusing on the experience of SMART education. For analysis purpose, a structural equation model, which was expanded from the theory of reasoned action, was presented. In addition, the difference in path coefficient, which affects the use intention of SMART education, was compared based on the experience with or without SMART education. The results showed that teacher efficacy in the teacher group without SMART education experience was more negatively significant in class burden. In the teacher group with SMART education experience, it was found that the attitude toward SMART education was more significant in use intention; organizational citizenship behavior was more significant in use intention; teacher efficacy was more significant in the attitude toward SMART education and; teacher efficacy was more significant in organizational citizenship behavior. No significant difference was found in the path coefficient among the groups in other hypotheses. Through these results, the factors for introducing and promoting SMART education and its invigoration measures were presented.

• Journal of the Korean Chemical Society
• /
• v.63 no.6
• /
• pp.459-472
• /
• 2019

The purpose of this study was to investigate the effect of science writing for different audiences on preservice chemistry teachers' chemistry concept understanding and modeling ability in general chemistry experiment activities using Argument-based Modeling (AbM) strategy. And we also examined preservice chemistry teachers' perceptions of modeling in different audience groups. The participants of the study were 18 university students in the first grade of preservice chemistry teachers taking a general chemistry experiment course. They completed eleven topics of general chemistry experiment using argument-based modeling strategy. The understanding of chemistry concept was compared with the effect size of pre- and post-chemistry concept test scores. To find out modeling ability, we analyzed level of model by each preservice chemistry teacher. Analytical framework for the modeling ability was composed of three elements, explanation, representation, and communication. The questionnaire was conducted to check up on preservice chemistry teacher's recognition of modeling. The result of analyzing the effect of modeling for different audience on the understanding of chemistry concept and modeling ability, the preservice chemistry teachers' were found to be more effective when the level of audience was low. There was no difference in the recognition of modeling between the groups for audience. However, we could confirm that the responses of preservice chemistry teachers are changed in concrete when they have an experience in succession on modeling.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.113-127
• /
• 2012

Face detection methods using image processing have been proposed in many different ways. Generally, the most widely used method for face detection is an Adaboost that is proposed by Viola and Jones. This method uses Haar-like feature for image learning, and the detection performance depends on the learned images. It is well performed to detect face images within a certain distance range, but if the image is far away from the camera, face images become so small that may not detect them with the pre-learned Haar-like feature of the face image. In this paper, we propose the far distance face detection method that combine the Aadaboost of Viola-Jones with a saliency map and user's attention information. Saliency Map is used to select the candidate face images in the input image, face images are finally detected among the candidated regions using the Adaboost with Haar-like feature learned in advance. And the user's eye-tracking information is used to select the interest regions. When a subject is so far away from the camera that it is difficult to detect the face image, we expand the small eye gaze spot region using linear interpolation method and reuse that as input image and can increase the face image detection performance. We confirmed the proposed model has better results than the conventional Adaboost in terms of face image detection performance and computational time.

• Korean Journal of Construction Engineering and Management
• /
• v.18 no.2
• /
• pp.38-48
• /
• 2017

Finding causes of workers' unsafe behaviors is important to prevent construction accidents because 80 percent of accidents occur by workers' unsafe behaviors. In this regard, this research aims to investigate possible reasons of workers' unsafe behaviors based on workers' cognitive process model using System dynamics. This study is based on two ways of workers' cognitive process which are in relation to hazard perception and failure of hazard perception. Based on existing literature, causal loops for workers' cognitive process are developed to explain workers' habituation by staying out of accidents, safety learning by experience, failure of hazard perception, and attitude change by accidents. The interactions between the developed loops provide managerial insights to reduce workers' unsafe behaviors from a safety manager's perspective including increasing the probability of workers' hazard perception through knowledge management, maintaining workers' positive attitude toward safety, and controlling first-line supervisors to eliminate workers' unsafe behavior. The research allows us to better understand the causes and solutions of workers' unsafe behaviors in workers' cognitive perspectives.

• Archives of design research
• /
• v.19 no.1 s.63
• /
• pp.15-26
• /
• 2006

As the interest and use of computer have been increased among elderly users according to the entry to the aging society, the issue of universal design for computer usability was brought forward. Especially, it is widely recognized that universal UI(User Interlace) design on MS windows is important. The objective of this study is to conceptualize Universal GUI design on MS windows which can be used more easily and intuitively by novice users such as elderly and housewives. Especially, the solution for Universal GUI design on MS windows was developed by reflecting elderly users' needs because elderly users is the group with more difficulties than other user groups in learning and using MS windows. First, elderly user's needs was collected by participant observation as a teaching assistant in computer dass for elderly people for 4 weeks. Secondly, the experimental test and in-depth interview was implemented to find difficulties factors and needs in addition to participant observation. Based on the findings, the new GUI design solution was suggested. The design solution consists of ideas in several categories such as setting default, simplification of function for easy conceptual model making, customization of function and working environment, and intuitive GUI in interaction process. The new MS windows GUI design can be accessed by novice mode when user login in window XP. This study has the significance in finding elderly users detailed needs through in-depth and long term participant observation. However, the usability of the suggested prototype needs to be verified to various user groups besides elderly users in the future.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.85-94
• /
• 1999

지식경영을 효과적으로 수행하기 위해서 기업은 지식경영을 구성하고 있는 요소를 정확히 이해할 필요가 있고, 이러한 중요 요소에 따라 투자가 이루어져야 한다. 본 연구는 지식경영의 중요 요소들을 제시함으로써, 앞으로 지식경영을 계획하고 있는 기업이 효과적으로 지식경영을 추진할 수 있는 활동 지침 및 투자 방향을 제시하고자 한다. 이를 위해, 본 연구에서는 각종 국내외 지식경영 관련 문헌에서 논의된 사항을 중심으로, 지식경영을 구성하는 30개의 중요요소를 추출하고, 분석계층도(AHP)를 이용하여 지식경영을 달성하기 위한 요소들을 위계적 구조로 정리하고, 최종단계에서 238개의 지식경영 구현의 평가기준을 마련하였다. 또한 실제로 지식경영 구현 요소들의 상대적 중요성을 파악하기 위해, 먼저 국내에서 지식경영을 추진하고 있거나 관심을 보이고 있는 48개 기업의 담당자 및 관련 부서원을 대상으로 설문조사를 실시하였고, 동시에 지식경영을 실제로 수행하고 있는 13개 기업의 담당자를 대상으로 각 기업에서 추진하고 있는 지식경영의 현황 파악을 위해 지식경영 실천의 평가기준에 대한 설문을 실시하였다. 이 두 가지 설문 조사 결과를 종합해 볼 때, 기업에서는 지식경영 구현 요소 중에서 인프라 내의 프로세스와 프로세스를 구성하는 지식의 활용과 전파 등이 중요하다고 인식하고 있는 반면, 실제로는 인프라 내의 정보기술과 프로세스를 구성하는 다른 한 축인 지식의 창출과 축적 면에 투자가 이루어진 것으로 나타났다. 이 외에도 지식화, 성과와 가치의 연계 그리고 지식의 가시화 등의 요소들은 상대적 중요도 인식과는 반대로 지식경영 추진에 있어 외면당하고 있는 것으로 나타났다. 따라서 본 연구는 지식 경영의 이러한 불균형을 시정할 수 있는 방향으로 앞으로의 투자가 수행되어야 할 것을 제안하고 있다. 산업의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적 중률을 나타내었다.(ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation model of the HAN projects including scientific and technological effects. Since the HAN projects consists of 18 subprograms, it is difficult In evaluate all the subprograms

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.3
• /
• pp.102-113
• /
• 2016

This study analyzes the geographic information system (GIS) and machine learning models to understand the relationship between the appearance of hibernation sites and habitats in order to systematically manage the habitat of Asiatic Black Bear(Ursus thibetanus ussuricus) inhabiting Jirisan National Park, South Korea. The most important environmental factors influencing the hibernation sites was found to be the inclination(41.4%), followed by altitude(20.4%), distance from the trail(10.9%), and age group(7.7%) in the order of their contribution. A comparison between the hibernation habitat and the normal habitat of Asiatic Black Bear indicated that the average altitude of the hibernation sites was 63m, whereas the average altitude of the normal habitat was approximately 400m. The average inclination was found to be $7^{\circ}$, and a preference for the steeper inclination of $12-43^{\circ}$ was also observed. The average distance of the hibernation site from the road was approximately 300m; the range of separation distance was found to be 1,300-2,400m. This was thought to be the result of a safer selection of winter hibernation site by preventing human contact and outside invasion. This study analyzes the habitat environmental factors for the selection of hibernation sites that prevent severe cold and other threats during the hibernation period in order to provide fundamental data for hibernation ecology and habitat management of Asiatic Black Bear.

• Journal of The Korean Association of Information Education
• /
• v.19 no.1
• /
• pp.45-56
• /
• 2015

This article examines the effects of a long term robot based instruction on the creativity of elementary students. To explain these effects, we conducted similar creativity test twice to 237 students of schools which had been designated as a robot based instruction model from 2011 to 2012. From these test results, the following three conclusions may be drawn: (1) The creativity of students who had participated in long term robot based instruction increased significantly, especially after the first test. (2) The fluency and originality as two of the sub-creativity factors are also accelerated significantly, especially after the first test. (3) The creativity of male and female students are all improved significantly but the test period factor and the interaction factor between male and female are not significant. (4) All elementary students of the lower grades(1st and 2nd grades), middle grades(3rd and 4th grades) and higher grades(5th and 6th grades) increased significantly but the test period factor and the interaction factor between the grades were not significant. On the other hand, the creativity improvement between lower-middle grades and higher grades is significant.