• Journal of Popular Narrative
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• v.25 no.2
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• pp.247-275
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• 2019

We hope that more diverse interests will arise in the novels by Park Gye-hyeong By looking at writers and works in time, we identified the key element of Park Gye-hyeong's novels as 'romantic'. Romantic nature of this time is lyrical, sentimental, spiritual, unrealistic and idealistic. Based on a romantic understanding of the world, the core sanction of the novel is love, focusing on feelings of sadness, and on the aspects of joy, separation, and pain that arise from loving relationships rather than the aspects of joy. Based on the feelings of grief, the novels end with failure, death and betrayal, thus embodying tragic romanticism. Before her marriage, Park Gye-hyeong's novels were love stories that revealed her longing for beautiful love based on sensibility. The idyllic world and longing for nature reveal a romantic world-view. Ultimately, it is a fictional worldview that the author seeks to despair and long for, and to find the sincerity and morality of love in an environment that does not. Park Gye-hyeong, who became a housewife, expressed that she wanted to write a piece that can give readers a sense of nostalgia by embodying "romance at a high level," not "sentimental." In subsequent works, physical relationships are treated as failures of love and spiritual relationships as the fruit of love, revealing the lofty spirituality, idealistic longing and religious nature of love. Park Gye-hyeong confessed her shame about her previous work when she published a new one after more than two decades of writing. And after more than two decades of reflection, her new novel had a new theme of "recovering destroyed humanity." However, the search for "humanity" in the two novels released after the write-off tends to be somewhat hasty at the end of the novel. The question of human nature, sin and forgiveness, is the next best thing to save as a way of life, rather than as a result of the intense inner agony and behavior of the characters within the narrative, and this also shows a sudden shift in religiousness at the end of the novel. Therefore, the romantic meaning of the superficial is superficial.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.159-172
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• 2023

The purpose of this study is to explore a collaborative start-up space(Co-Space) configuration plan in terms of university-regional linkage through demand analysis on the composition of university-regional linkage startup space. To this end, a survey was conducted for request analysis, and the collected data were analyzed through the t-test, The Lotus for Focus model. In addition, FGI was implemented for entrepreneurs, and the direction of the composition of the university-region Co-Space was derived from various aspects. The results of this study are as follows. First, as a result of the analysis of the necessity of university-community Co-Space, the necessity of opening up the start-up space recognized by local residents and the necessity of building the start-up space in the region were high. In addition, men recognized the need to build a space for start-ups in the community more highly than women did women. Second, as a result of analysis of demands for university-regional Co-Space, the difference between current importance and future necessity of university-regional Co-Space was statistically significant. Third, as a result of analysis on the composition of the startup space by cooperation between universities and regions, different demands were made for composition of the startup space considering openness and closeness, and for composition of the startup space size. The implications of the study are as follows. First, Co-Spaces need to be constructed in conjunction with universities in accordance with the demands of start-up companies in the region by stage of development. Second, it is necessary to organize a customized Co-Space that takes into account the size and operation of the start-up space. Third, it is necessary to establish an experience-based open space for local residents in the remaining space of the university. Fourth, it is necessary to establish a Co-Space that enables an organic network between local communities, start-up investment companies, start-up support institutions, and start-up companies. This study is significant in that it proposed the regional startup ecosystem and the cooperative start-up space structure for strengthening start-up sustainability through cooperation between universities and local communities. The results of this study are expected to be used as useful basic data for Co-Space construction to build a regional start-up ecosystem in a trend emphasizing the importance of start-up space, which is a major factor affecting start-up companies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• (The) Research of the performance art and culture
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• no.42
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• pp.47-75
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• 2021

This thesis looks into the interpretation of the Korean War and mystery genre in Korea in the 1970s by analyzing the special drama , in which the theme was directly related to the Korean War, airing through MBC in 1979. It begins by finding the change in direction in the 1970s when the world of TV was dictated through the heavy censorship and the memory of the war by the government. It also looks at the intentions of the producer who was taking in the new way and the viewers who also accepted this drama and its reflections. In order to gain some insights into these issues, it compares between the drama "The Last Witness" and the original novel by Seong-jong Kim who holds the same time to see the way in which this is dramatized. The drama, "The Last Witness", was produced with a plan to generate a high-quality special drama which combined both artistry and sense of purpose. Nevertheless, as watching TV became a leisurely past-time during this period, TV dramas become more aggressive and suggestive in order to attract viewers. This ultimately was encored with obstacles due to the regime and the heavy censorship at the time. The genre of special drama that is well known in South Korea, is designed as an art form to satisfy both their unique artistry and its purpose. The conflict is seen between the key elements of the artistic drama crated by the producers and the 'encouraged' elements that often are needed to engage the viewers. Thus, more often than not, special dramas defeat the original intention of national harmony, encouraged by the regime. This is due to the 'novelty' aspect which grows from the effort of bringing enjoyment to viewers whilst also trying to achieve the artistic drama to life. Alongside this, crime element in this drama is designed in a way that visually embodies the process of deduction, becoming a new possibility to secure the reality of the times. However, it was also a paradoxical existence since it was indicated as an example of unrefined culture that lost its original intention. In that way, it is worth to think that detective suspense stories, which were not popular in Korea, influenced viewers as a tv drama series in the 1970s through the various elements that compose the genre. They went through a process of transplantation and acceptance whilst also attempting to satisfy the viewers and their encouraged elements to engage them. As is well known, crime drama in Korea has its own style by mixing anticommunism and detective reasoning. This combination is found in the way in which the genre naturally forms through the elements selected and excluded in the dramatization of "The Last Witness". The point is that the special drama "The Last Witness" can be seen as an intermediate form that shows the tendency of transformation from the detective reasoning form alongside the crime aspects as TV dramas began to include anticommunism messaging and investigation in the 1970s. In conclusion, when the detective reasoning is used as an element in a TV drama, it shows the trust of the public system and it constantly seeks the possibility of circumventing the political interpretation. The memories of the war is seen as a tool that neutralizes the dismal imaginations inscribed on the dark side of society and the system. As a result, "The Last Witness", broadcasted at the end of the Yushin regime in Korea, is a strange result which combines the logic of a special drama and the encouraged characteristics of television dramas. The viewers' desire which is the discussion about the hidden traces from the texts needs to be restored again.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.119-135
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• 2021

Korea has stepped up efforts to investigate and catalog its flora and fauna to conserve the biodiversity of the Korean Peninsula and secure biological resources since the ratification of the Convention on Biological Diversity (CBD) in 1992 and the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits (ABS) in 2010. Thus, after its establishment in 2007, the National Institute of Biological Resources (NIBR) of the Ministry of Environment of Korea initiated a project called the Korean Indigenous Species Investigation Project to investigate indigenous species on the Korean Peninsula. For 15 years since its beginning in 2006, this project has been carried out in five phases, Phase 1 from 2006-2008, Phase 2 from 2009-2011, Phase 3 from 2012-2014, Phase 4 from 2015-2017, and Phase 5 from 2018-2020. Before this project, in 2006, the number of indigenous species surveyed was 29,916. The figure was cumulatively aggregated at the end of each phase as 33,253 species for Phase 1 (2008), 38,011 species for Phase 2 (2011), 42,756 species for Phase 3 (2014), 49,027 species for Phase 4 (2017), and 54,428 species for Phase 5(2020). The number of indigenous species surveyed grew rapidly, showing an approximately 1.8-fold increase as the project progressed. These statistics showed an annual average of 2,320 newly recorded species during the project period. Among the recorded species, a total of 5,242 new species were reported in scientific publications, a great scientific achievement. During this project period, newly recorded species on the Korean Peninsula were identified using the recent taxonomic classifications as follows: 4,440 insect species (including 988 new species), 4,333 invertebrate species except for insects (including 1,492 new species), 98 vertebrate species (fish) (including nine new species), 309 plant species (including 176 vascular plant species, 133 bryophyte species, and 39 new species), 1,916 algae species (including 178 new species), 1,716 fungi and lichen species(including 309 new species), and 4,812 prokaryotic species (including 2,226 new species). The number of collected biological specimens in each phase was aggregated as follows: 247,226 for Phase 1 (2008), 207,827 for Phase 2 (2011), 287,133 for Phase 3 (2014), 244,920 for Phase 4(2017), and 144,333 for Phase 5(2020). A total of 1,131,439 specimens were obtained with an annual average of 75,429. More specifically, 281,054 insect specimens, 194,667 invertebrate specimens (except for insects), 40,100 fish specimens, 378,251 plant specimens, 140,490 algae specimens, 61,695 fungi specimens, and 35,182 prokaryotic specimens were collected. The cumulative number of researchers, which were nearly all professional taxonomists and graduate students majoring in taxonomy across the country, involved in this project was around 5,000, with an annual average of 395. The number of researchers/assistant researchers or mainly graduate students participating in Phase 1 was 597/268; 522/191 in Phase 2; 939/292 in Phase 3; 575/852 in Phase 4; and 601/1,097 in Phase 5. During this project period, 3,488 papers were published in major scientific journals. Of these, 2,320 papers were published in domestic journals and 1,168 papers were published in Science Citation Index(SCI) journals. During the project period, a total of 83.3 billion won (annual average of 5.5 billion won) or approximately US $75 million (annual average of US $5 million) was invested in investigating indigenous species and collecting specimens. This project was a large-scale research study led by the Korean government. It is considered to be a successful example of Korea's compressed development as it attracted almost all of the taxonomists in Korea and made remarkable achievements with a massive budget in a short time. The results from this project led to the National List of Species of Korea, where all species were organized by taxonomic classification. Information regarding the National List of Species of Korea is available to experts, students, and the general public (https://species.nibr.go.kr/index.do). The information, including descriptions, DNA sequences, habitats, distributions, ecological aspects, images, and multimedia, has been digitized, making contributions to scientific advancement in research fields such as phylogenetics and evolution. The species information also serves as a basis for projects aimed at species distribution and biological monitoring such as climate-sensitive biological indicator species. Moreover, the species information helps bio-industries search for useful biological resources. The most meaningful achievement of this project can be in providing support for nurturing young taxonomists like graduate students. This project has continued for the past 15 years and is still ongoing. Efforts to address issues, including species misidentification and invalid synonyms, still have to be made to enhance taxonomic research. Research needs to be conducted to investigate another 50,000 species out of the estimated 100,000 indigenous species on the Korean Peninsula.

• Management & Information Systems Review
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• v.34 no.5
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• pp.1-21
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• 2015

This study is to testify whether according to the variables of population statistics there emerge differences in the ethical leadership, ethical climate, organizational citizenship behavior and LMX by Social Entrepreneurs. For this we have performed survey 652 social entrepreneurs at authorized Social Enterprises. The results of analysis are as follows. First, it was analyzed that the sub-factors of the ethical leadership, such as contribution and integrity by Social entrepreneurs have revealed significant difference according to gender. Also the sub-factor of ethical climate, such as utilitarianism has revealed significant difference by gender, too. The analysis result signifies no significance in OCB, LMX. Second, there were significant difference in the sub-factor of ethical leadership such as integrity, the sub-factor of ethical climate such as, utilitarianism, the sub-factor of OCB such as effectiveness, and LMX by religious. Third, the analysis result signifies no significance in ex-work place, whether they worked at Social enterprise or not. Finally, as the result of our analysis whether there are any differences according to gender, religious, and ex-work place of the Social entrepreneurs in the ethical leadership, ethical climate, OCB, and LMX by Social entrepreneurs, it was revealed that there are some significant. This study emphasizes the importance of ethical leadership, ethical climate, OCB and LMX. It reveled these variables have differences by demographic characteristics of Social entrepreneurs. In conclusion, this study suggests that the consideration of the variables of population statistics according to the various variables about Social enterprise.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.193-214
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• 2017

This study was carried out to identify the reality of students' understanding of Korean traditional scientific knowledge (TSK), the educational contexts influenced their understanding of TSK, and their sense of value of TSK, through the science activity 'Being a commentator on Korean traditional scientific culture' as a way of finding direction to apply TSK to science education while maintaining the inherent meaning of our traditional science. Seventh grade students have discovered TSK contents in Changdeok Palace, prepared their own scripts for seven months, and finally, explained to fifth to sixth grade students. The video recordings of all lessons, scripts of explanation, reports of field activities, and individual interviews were all analyzed. Students understood TSK in four viewpoints: the traditional view of nature, the traditional science and technology, the traditional life using science, and the natural science contents. During their activities, communication with peers or elders both in classroom and in Changdeok Palace, the interaction with place, and the sense of responsibility as a commentator helped students understand the scientific aspects of TSK, form contextual and sensible scientific knowledge, and apprehend various scientific explanations of contents. Depending on their internalization of experiences, the students' experiences produced three types of interpretation: delivery, persuasion, and understanding. Students formed their TSK sense of value as scientific characteristics, the need of new perspective about science, the need to protect and maintain TSK as our culture. The results of this study show that TSK can provide integrated and actual contextual education in science education and can be used to understand the cultural diversity of scientific and scientific methods and the characteristics of oriental scientific thinking. In addition, the simultaneous approach of TSK and school science to traditional culture can contribute to ideal concept formation and subjective attitude toward our traditional culture.