• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.63-78
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• 2014

Integrative STEM education is an engineering design-based learning approach that purposefully integrates the content and process of STEM disciplines and can extend its concept to integration with other school subjects. This study was part of fundamental research to develop an integrative STEM education program based on the science inquiry process. The specific objectives of this study were to review relevant literature related to STEM education, analyze the key elements and value of STEM education, develop an integrative STEM education model based on the science inquiry process, and suggest an exemplary program. This study conducted a systematic literature review to confirm key elements for integrative STEM education and finally constructed the integrative STEM education model through analyzing key inquiry processes extracted from prior studies. This model turned out to be valid because the average CVR value obtained from expert group was 0.78. The integrative STEM education model based on the science inquiry process consisted of two perspectives of the content and inquiry process. The content can contain science, technology, engineering, and liberal arts/artistic topics that students can learn in a real world context/problem. Also, the inquiry process is a problem-solving process that contains design and construction and is based on the science inquiry. It could integrate the technological/engineering problem solving process and/or mathematical problem solving process. Students can improve their interest in STEM subjects by analyzing real world problems, designing possible solutions, and implementing the best design as well as acquire knowledge, inquiry methods, and skills systematically. In addition, the developed programs could be utilized in schools to enhance students' understanding of STEM disciplines and interest in mathematics and science. The programs could be used as a basis for fostering convergence literacy and cultivating integrated and design-based problem-solving ability.

• Journal of The Korean Association For Science Education
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• v.22 no.4
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• pp.872-881
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• 2002

Student questioning is included in the priority of science literacy, to enable students to solve problems by exploring questions, communicating and constructing knowledge(AAAS, 1989). Also, the essence of student questioning in science lies in its function as a link between thinking and learning. But educators did not pay much attention to students' questioning in Korea. The purpose of this study was to investigate the effects of science question enhancement instruction on students' science questioning level and achievement. Also, this study showed the effects of other variables(logical thinking, science achievement, interest, and gender) on students' science questioning level. The pretest-posttest control group design group design was used. The sample was consisted of 80 second grade middle school students in experimental group(Science question enhancement instruction) and 74 students in control group(Traditional learning). Students in both groups were received identical content instruction on the unit 'Structures and functions of plant'. These groups were treated for 15 hours during 6 weeks. Students' questions were rated using the four levels described by the Middle School Students' Science Question Rating Scale(r= .96,)(Cuccio-Schirripa & Steinner, 2000). Science achievement data were collected using a 17 item multiple choice test(Cronbach ${\alpha}$= .84). To investigate students' logical thinking ability, a abridged GALT(Cronbach ${\alpha}$= .69) was used. Five-way ANOVA, ANCOVA, and multiple regression analysis were used to analyze the results. The results indicated that students who received instruction on researchable questioning outperformed those students who were not instructed on high-order questioning(p< .01). Results of correlations indicated that instruction(r= .640), science achievement(r= .311) and logical thinking ability(r= .212) was significantly and positively related with students' questioning level. But, interest and gender did not show any significant correlation with students' questioning level. Science question enhancement instruction was more effective on science achievement than the traditional instruction(p< .01).

• Journal of the Korean Dietetic Association
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• v.15 no.2
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• pp.97-112
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• 2009

The purpose of this study was to develop a nutrition education curriculum with teacher's guide which includes discretionary activities for obese children. A survey was carried out to investigate the recognition of body image and food behaviors according to the obesity index (mild, moderate, severe) in school children (4~6th grade, 158 boys and 60 girls) who were selected based on a physical examination in May, 2006 in the Gyeongnam province. Next, a nutrition education curriculum with teacher's guide was developed on the basis of the findings from the survey and from preceding researches. The results are summarized as follow. The results of this study showed the existence of some nutritional problems such as overeating, prejudice, skipping meals, snacking patterns, etc, which indicate the need for nutritional management for obese children. Most overweight children (80.3%) showed the most interest in the nutrition education program, particularly with regards to dieting for weight control (64.7%). The developed nutrition education curriculum consisted of 8 main subjects and 13 subtitles. The curriculum was prepared for 13 lessons and included songs and singing, making-up lyrics, games about nutrition, discussions of the experience of eating (satiety, thirst, hunger), debates on dietary habits, writing and others to promote the interest for learning. We aimed to develop this program in an attempt to improve the dietary habits of obese school children. This is very important because once a dietary habit is formed in adults, it is difficult to change and the best adjustable stage is during childhood. Therefore, early nutrition education during elementary school can change and build-up the awareness of health in young elementary school children.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.91-116
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• 1991

Market liberalization progressing simultaneously with high and rapidly rising domestic wages has created an adverse business environment for domestic firms. Korean firms are losing their international competitiveness in comparison to firms from LDC(Less Developed Countries) in low-tech industries. In high-tech industries, domestic firms without government protection (which is impossible due to the liberalization policy and the current international status of the Korean economy) are in a disadvantaged position relative to firms from advanced countries. This paper examines the division of roles between the private sector and the government in order to achieve a successful structural adjustment, which has become the impending industrial policy issue caused by high domestic wages, on the one hand, and the opening of domestic markets, on the other. The micro foundation of the economy-wide structural adjustment is actually the restructuring of business portfolios at the firm level. The firm-level business restructuring means that firms in low-value-added businesses or with declining market niches establish new major businesses in higher value-added segments or growing market niches. The adjustment of the business structure at the firm level can only be accomplished by accumulating firm-specific managerial assets necessary to establish a new business structure. This can be done through learning-by-doing in the whole system of management, including research and development, manufacturing, and marketing. Therefore, the voluntary cooperation among the people in the company is essential for making the cost of the learning process lower than that at the competing companies. Hence, firms that attempt to restructure their major businesses need to induce corporate-wide participation through innovations in organization and management, encourage innovative corporate culture, and maintain cooperative labor unions. Policy discussions on structural adjustments usually regard firms as a black box behind a few macro variables. But in reality, firm activities are not flows of materials but relationships among human resources. The growth potential of companies are embodied in the human resources of the firm; the balance of interest among stockholders, managers, and workers of the company' brings the accumulation of the company's core competencies. Therefore, policymakers and economists shoud change their old concept of the firm as a technological black box which produces a marketable commodities. Firms should be regarded as coalitions of interest groups such as stockholders, managers, and workers. Consequently the discussion on the structural adjustment both at the macroeconomic level and the firm level should be based on this new paradigm of understanding firms. The government's role in reducing the cost of structural adjustment and supporting should the creation of new industries emphasize the following: First, government must promote the competition in domestic markets by revising laws related to antitrust policy, bankruptcy, and the promotion of small and medium-sized companies. General consensus on the limitations of government intervention and the merit of deregulation should be sought among policymakers and people in the business world. In the age of internationalization, nation-specific competitive advantages cannot be exclusively in favor of domestic firms. The international competitiveness of a domestic firm derives from the firm-specific core competencies which can be accumulated by internal investment and organization of the firm. Second, government must build up a solid infrastructure of production factors including capital, technology, manpower, and information. Structural adjustment often entails bankruptcies and partial waste of resources. However, it is desirable for the government not to try to sustain marginal businesses, but to support the diversification or restructuring of businesses by assisting in factor creation. Institutional support for venture businesses needs to be improved, especially in the financing system since many investment projects in venture businesses are highly risky, even though they are very promising. The proportion of low-value added production processes and declining industries should be reduced by promoting foreign direct investment and factory automation. Moreover, one cannot over-emphasize the importance of future-oriented labor policies to be based on the new paradigm of understanding firm activities. The old laws and instititutions related to labor unions need to be reformed. Third, government must improve the regimes related to money, banking, and the tax system to change business practices dependent on government protection or undesirable in view of the evolution of the Korean economy as a whole. To prevent rational business decisions from contradicting to the interest of the economy as a whole, government should influence the business environment, not the business itself.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.142-161
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• 2013

In order to enhance scientific interest and a sense of affinity about ocean, the programs of the oceanic camp 'oceanic summer school' were developed and applied to $4^{th}$ and $9^{th}$-grade elementary and middle school students for 9 years from 2004 to 2012. It was composed of oceanic training for snorkeling, a tour to oceanic institutes and museums near the camp academy place, experimental learning in oceanic-related field, field trips for ocean and earth sciences, and lectures on various subjects of ocean. We developed and implemented 9-kinds of inquiry surveys to evaluate changes in cognitive and affective characteristics, and ocean literacy of students participated at the present oceanic summer camp. Based on the statistical analysis, affective characteristics such as interest, inquisitiveness, passion, and so on, were enhanced. Analysis of ocean literacy revealed that cognitive characteristics of the students were increased by 40%. We presented parents' responses on the programs of oceanic summer school. Some students with less initial interest of ocean have positively changed to make up their minds to be a oceanographer in several years later. In light of this, the summer school can be evaluated to be successfully functioned as a long-term support system for potentially young-talented students in the field of ocean science. This study addresses that long-term implementation of the summer oceanic camp may trigger students with potential talent toward in-depth science in the near future even though it could not bring positive effect immediately. This addresses the necessity of policy supports in order that various programs like the scientific camp should be more constructively developed and executed for next-generation manpower in oceanographic fields.

• Cartoon and Animation Studies
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• s.36
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• pp.437-467
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• 2014

This study is intended to reveal reading comic books helps improve creativity. Though the long-lasting negative recognition towards comic books has positively changed these days, we need a ground upon which the social recognition needs improvement in that children's comic books have been used as a learning tool. Its introduction points out that there has been shortage of empirical researches on comic book reading, and as one of the empirical research methods, presents a method of comparative analysis on comic book reading, school study, and creativity tests via survey. The theoretical background in the 2nd chapter, first, puts emphasis on the significance of the creativity theory among all the other theories related to creativity, which focuses on problem-solving capacity. Second, it theoretically reviews the meaning which 'fun' and 'interest' have in development of creativity in the context of developmental process of the modern educational theories. Third, it empathizes that traits of reading comic books start off with 'fun' and 'interest', that awareness of reality gets expanded via the process of characters making their way through a strange world with empathy and absorption, and that comic book reading has to do with creativity. Fourth, it presents a model questionnaire with which to study relationship between comic books and creativity in an empirical way. The analysis on the survey outcome in the 3rd chapter shows, first, that smart students read many comic books, not to mention that studying helps improve creativity, which indicates above all, comic book reading and improvement of creativity are not negatively related, but are mutually complementary. Second, that creativity enhanced by reading comic books is higher than that enhanced by studying, which may mean comic book reading is more effective than studying in developing creativity. It has drawn a conclusion based upon these results, that reading comic books bears positive efficacy on both studying and developing creativity. Standing on this conclusion, it proposes it necessary to develop methods by grades of educating how to read comic books and to provide a recommended list of comic books to read.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.