• Journal of The Korean Association For Science Education
• /
• v.32 no.1
• /
• pp.129-145
• /
• 2012

This study investigated factors of creativity and interaction between factors that are revealed when gifted students designed scientific experiments. For this, we firstly developed items which required the written process of designing experiments to explore creativity factors. Then, we used these items as a part for letters of self-introduction to students who applied for 2011 correspondence education of general physics for the Korea Physics Olympiad. 513th letters of self-introduction which were analyzed to investigate factors of creativity in view of creativity definition after researchers' consultation, which specifically means a combination of divergent and convergent thinking. The results were as follows; (1) in the step of hypothesis building, we could not only find Originality and the Flexibility & Fluency, which were factors of divergent thinking, but also Coherency and Elaborateness, which were factors of convergent thinking. (2) in the step of the hypothesis testing, we could explore Originality, Flexibility & Fluency in divergent thinking and Coherency, Reliability, Clarity, Elaborateness in convergent thinking. (3) we also figured out three creativity types of gifted students from the viewpoint that creativity is a consequence of interaction between divergent thinking and convergent thinking; a) Type A showed divergent and convergent factors of creativity in the step of hypothesis building. However, type A did not include divergent factors of creativity on the process of the hypothesis testing. b) Type B had divergent and convergent factors of creativity on the process of the hypothesis testing, but it had not convergent factors of creativity on the step of hypothesis building. c) Finally, in Type C, only divergent factors of creativity appeared on the process of the hypothesis testing, but convergent factors of creativity could be found on the step of hypothesis building and hypothesis testing.

• Journal of The Korean Association For Science Education
• /
• v.34 no.5
• /
• pp.499-510
• /
• 2014

The purpose of this research is to understand interactive learning during docent-led tours in a science museum focusing on scaffolding. We developed a scaffolding framework by collating the work of other researchers in related fields. The results show that scaffolding included three dimensions: purpose, interaction, and domain. The purpose dimension, divided into six categories, is related to the intention of the scaffolder and what the scaffolding are for: strategic, social, procedural, conceptual, verbal, and metacognitive. The interaction dimension reflects students' interaction with the scaffolder in two ways: dynamic (situation specific) and static (planned in advance). The domain dimension is related to two contents: domain-general and domain-specific (such as science). The scaffolding framework was applied to dynamic interactions between docents and visitors. The data was collected from elementary school students' family visits with the guidance of two docents at the Seodaemun Museum of Natural History. The data collected consisted of surveys, interviews, video-recordings, and transcripts. The analysis shows that five guiding contexts and scaffolding phases were recognized; 1) strategic scaffolding in a poorly illustrated exhibit; 2) conceptual scaffolding in a thoroughly explanative exhibit; 3) verbal scaffolding in misleading interpretation; 4) procedural scaffolding in a manipulative exhibit; and 5) metacognitive scaffolding with inaccurate content. In addition, the results show that the docents used the dynamic and static scaffolding synthetically so that the docent-led tour was effective. In conclusion, this study presents the usefulness of understanding visitors' science learning through the scaffolding framework, as well as the how docents can scaffold actively.

• Korean Journal of Applied Biomechanics
• /
• v.12 no.2
• /
• pp.259-278
• /
• 2002

This study divided straight success, pade success and failure with 7male golfers who have experiences more than 3 years, analyzed kinematic factors of golf swing to suggest scientifically. The conclusions were follows. 1) The wrist angle has significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 2) The body twist angle has no significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 3) The shoulder joint rotation angle has no significant difference in success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 4) The left hip joint vertical angle has no significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 5) The hip joint rotation angle has no significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 6) The trunk angle has no significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. 7 )The left knee joint angle has no significant difference in straight success, pade success and failure when swing of every pattern. There is no significant difference in pade success and failure. This study divided golf swing motion of pattern change in straight success, pade success and failure and analyzed the kinematic factors by 3-dimension cinematography to improve performance. In the future, many researchers have to study kinematic analysis to improve performance in every events.

• Journal of Korean Society of Environmental Engineers
• /
• v.35 no.11
• /
• pp.835-860
• /
• 2013

Recently, the consumption of nanomaterials has been significantly increased in both industrial and commercial sectors, as a result of steady advancement in the nano-technologies. This ubiquitous use of nanomaterials has brought up the concern that their exposure to environments may cause detrimental effects on human health as well as natural ecosystems, and it is required to characterize their behavior in various environmental media and to evaluate their ecotoxicity. For the sake of accomplishing those assessments, the development of methods to effectively separate them from diverse media and to quantify their properties should be requisitely accompanied. Among a number of separation techniques developed so far, this study focuses on Field-Flow Fractionation (FFF) because of its strengths, such as relatively less disturbance of samples and simple pretreatment, and we review overseas and domestic literatures on the separation of nanomaterials using the FFF technique. In particular, researches with Flow Field-Flow Fractionation (FlFFF) are highlighted due to its most frequent application among FFF techniques. The basic principle of the FlFFF is briefly introduced and the studies conducted so far are classified and scrutinized based on the sort of target nanomaterials for the purpose of furnishing practical data and information for the researchers struggling in this field. The literature review suggests that the operational conditions, such as pretreatment, selection of membrane and carrier solution, and rate (velocity) of each flow, should be optimized in order to effectively separate them from various matrices using the FFF technique. Moreover, it seems to be a prerequisite to couple or hyphenate with several detectors and analyzers for quantification of their properties after their separation using the FFF. However, its application has been restricted regarding the types of target nanomaterials and environmental media. Furthermore, domestic literature data on both separation and characterization of nanomaterials are extremely limited. Taking into account the overwhelmingly increasing consumption of nanomaterials, the efforts for the area seem to be greatly urgent.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.255-277
• /
• 2011

Product Service System(PSS), which is an integrated combination of product and service, provides new value to customer and makes companies sustainable as well. The objective of this paper draws Critical Successful Factors(CSF) of PSS through multiple case study. First, we review various concepts and types in PSS and Platform business literature currently available on this topic. Second, after investigating various cases with the characteristics of PSS and platform business, we select four cases of 'iPod of Apple', 'Kindle of Amazon', 'Zune of Microsoft', and 'e-book reader of Sony'. Then, the four cases are categorized as successful and failed cases according to criteria of case selection and PSS classification. We consider two methodologies for the case selection, i.e., 'Strategies for the Selection of Samples and Cases' proposed by Bent(2006) and the seven case selection procedures proposed by Jason and John(2008). For case selection, 'Stratified sample and Paradigmatic cases' is adopted as one of several options for sampling. Then, we use the seven case selection procedures such as 'typical', 'diverse', 'extreme', 'deviant', 'influential', 'most-similar', and 'mostdifferent' and among them only three procedures of 'diverse', 'most?similar', and 'most-different' are applied for the case selection. For PSS classification, the eight PSS types, suggested by Tukker(2004), of 'product related', 'advice and consulancy', 'product lease', 'product renting/sharing', 'product pooling', 'activity management', 'pay per service unit', 'functional result' are utilized. We categorize the four selected cases as a product oriented group because the cases not only sell a product, but also offer service needed during the use phase of the product. Then, we analyze the four cases by using cross-case pattern that Eisenhardt(1991) suggested. Eisenhardt(1991) argued that three processes are required for avoiding reaching premature or even false conclusion. The fist step includes selecting categories of dimensions and finding within-group similarities coupled with intergroup difference. In the second process, pairs of cases are selected and listed. The second step forces researchers to find the subtle similarities and differences between cases. The third process is to divide the data by data source. The result of cross-case pattern indicates that the similarities of iPod and Kindle as successful cases are convenient user interface, successful plarform strategy, and rich contents. The differences between the successful cases are that, wheares iPod has been recognized as the culture code, Kindle has implemented a low price as its main strategy. Meanwhile, the similarities of Zune and PRS series as failed cases are lack of sufficient applications and contents. The differences between the failed cases are that, wheares Zune adopted an undifferentiated strategy, PRS series conducted high-price strategy. From the analysis of the cases, we generate three hypotheses. The first hypothesis assumes that a successful PSS system requires convenient user interface. The second hypothesis assumes that a successful PSS system requires a reciprocal(win/win) business model. The third hypothesis assumes that a successful PSS system requires sufficient quantities of applications and contents. To verify the hypotheses, we uses the cross-matching (or pattern matching) methodology. The methodology matches three key words (user interface, reciprocal business model, contents) of the hypotheses to the previous papers related to PSS, digital contents, and Information System (IS). Finally, this paper suggests the three implications from analyzed results. A successful PSS system needs to provide differentiated value for customers such as convenient user interface, e.g., the simple design of iTunes (iPod) and the provision of connection to Kindle Store without any charge. A successful PSS system also requires a mutually benefitable business model as Apple and Amazon implement a policy that provides a reasonable proft sharing for third party. A successful PSS system requires sufficient quantities of applications and contents.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.21-44
• /
• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korean earth science society
• /
• v.22 no.6
• /
• pp.528-547
• /
• 2001

The blastoporphyritic granite gneiss (BPGN) including much alkali-feldspar megacrysts occurs in Jiri mountains area, southwestern part of Sobaegsan massif, Korea. The BPGN is formed gneiss complexes with other gneisses in Precambrian. The BPGN was named as porphyroblastic gneiss with porphyroblasts of alkali-feldspar megacrysts by other researchers, but the BPGN includes of euhedral alkali-feldspars (microcline), and the boundary with the granitic gneiss represents sharp contact as intrusive relationship. The BPGN mainly composes of alkali-feldspar megacrysts, quartz, plagioclase, K-feldspar and biotite some almandine and accessary minerals are muscovite, chlorite, apatite, zircon and opaques. The alkali-feldspar is microcline with perthitic texture. An content of plagioclases show 30 to 40. Biotites occur two type, one is Brown biotite which shows compositional ranges of Mg/Fe+Mg ratios from 0.38 to 0.52, the other is Green Bt. which is retrograde product. Camels to be various sizes and shapes have composition of almandine with 73 to 80 mole percent, but represent retrogressive zoning from core (X$_{pyr}$: 15.9${\sim}$20.8) to rim (X$_{pyr}$:13.7${\sim}$15.9) to be evidence of retrograde metamorphism. Megacrysts of alkali-feldspar in the BPGN show rectangular shape of euhedral and some become ellipsoidal or spheroidal in shape and the average size up to 20 cm long. The megacryst includes of biotite, plagioclase and quartz, and rarely euhedral apatite as inclusions. In petrochemistry the BPGN represents granodiorite composition, characteristics of peraluminous S-type granitoid and calc-alkaline features.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.7 no.3
• /
• pp.1-16
• /
• 2012

Under the international business circumstance, global supply chain management is considered a vital strategic challenge to small and medium-sized enterprises(SMEs) suffering from deficient resources and capabilities to exploit overseas markets comparing with large corporations. That is because they can expand their business domains into overseas markets by establishing strategic alliances with global supply chain partners. Although a wide range of previous researches have emphasized the cooperative networks in the chain, most are ignoring the importance of developing relational characteristics such as trust and reciprocity with the partners. Besides, verifying the relational factors influencing firms' export performances, some studies proposed different and inconsistent factors. According to the social capital theory, which is the social quality and networks facilitating close cooperation of inter-individual and inter-organization, provides the integrated view to identify the relational characteristics in the aspects of network, trust and reciprocal norm. Meanwhile, a number of researchers shows that global entrepreneurship is the internal and intangible resource necessary to promote SMEs' internationalization. Upon closer examination, however, they cannot explain clearly its influencing mechanism in the inter-firm cooperative relationships. This study is to verify the effect of social capital accumulated within global supply chain on SMEs' qualitative and quantitative export performance. In addition, we shed new light on global entrepreneurship expected to be concerned with the formation of social capital and the enhancement of export performances. For this purpose, the questionnaires, developed through literature review, were collected from 192 Korean SMEs affiliated in Korean Medium Industries Association and Global Chief Executive Officer's Club focusing on their memberships' international business. As a result of multi-regression analysis, the social capital - network, trust and reciprocal norm shared with global supply chain partner - as well as global entrepreneurship - innovativeness, proactiveness and risk-taking - have positive effect on SMEs' export performances. Also global entrepreneurship affects positively social capital which has mediating effect partially in the relationship between global entrepreneurship and performances. These results means that there is a structural process - global entrepreneurship(input), social capital(output), and export performances(outcome). In other words, a firm should consistently invest in and develop the social capital with global supply chain partners in order to achieve common goals, establish strategic collaborations and obtain long-term export performances. Furthermore, it is required to foster the global entrepreneurship in an organization so as to build up the social capital. More detailed practical issues and discussion are made in the conclusion.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.95-105
• /
• 2013

Recently, SME's Collaboration activities have become one of a vital factor for sustaining competitive edge. This is because of the rapidly changing and competitive market environment, and also to leverage performance by overcoming obstacles of having limited internal resources. Discussing about the effects and relationships of the firm's collaboration activities and its outputs are not new. However, as ICT and various technologies have been diffused into the traditional industries, boundaries and practice capabilities within the industries are becoming ambiguous. Thus contents of the products/services and their development methods are also go and come over the industries. Although many researchers suggested the relations of SME's collaboration activities and innovation performances, most of the previous literatures are focusing on broad perspectives of firm's environmental factors rather than considering various SME's idiosyncrasy factors such as their major product and customer types at once. Therefore, the purpose of this paper is to analyze how SME(Small Medium Enterprise)'s external collaboration activities by their idiosyncrasy act as an input to types of innovation performance. In order to analyze collaboration effects in detail, we defined factors that can represent the SME's business environment - Perceived importance of using external resources, Perceived importance of external partnership, Collaboration and Collaboration levels of Major Product types, Customer types and lastly the Firm Sizes. We have also specifically divided the performance of innovation types as product innovation and process innovation based on existing research. In this study, the empirical analysis is based on Probit Regression Model to observe the correlations with the impact of each SME's business environment and their activities. For the empirical data, 497 samples were collected which, this sample data was extracted from the 'Korean Open Innovation Survey' performed by ETRI(Korean Electronics Telecommunications Research Institute) in 2010. As a result, empirical test results indicated that the impact of collaboration varies depend on the innovation types (Product and Process Innovation). The Impact of the collaboration level for the product innovation tend to be more effective when SMEs are developing for a final product, targeting on for individual customers (B2C). But on the other hand, the analysis result of the Process innovation tend to be higher than the product innovation, when SMEs are developing raw materials for their partners or to other firms targeting on for manufacturing industries(B2B). Also perceived importance of using external resources has effected to both product and process innovation performance. But Perceived importance of external partnership was statistically insignificant. Interesting finding was that the service product has negative effects on for the process innovation performance. And Relationship between size of the firms and their external collaboration activities with their performance of the innovations indicated that the bigger firms(over 100 of employees) tend to have better for both product and process innovations. Finally, implications of the results can be suggested as performance of innovation can be varied depends on firm's unique business idiosyncrasy as well as levels of external collaboration activities. The Implication of this research can be considered for firms in selecting an appropriate strategy as well as for policy makers.