• Journal of Science Education
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• v.35 no.1
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• pp.1-12
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• 2011

The purpose of this study was to examine the effects of science class using the multimedia materials on high school students' attitude toward science. The subjects were 222 high school students. For this study, 11th graders at a high school were assigned to a comparison group and an experimental group. The experimental group was received science class using multimedia materials for 3 months. The research design was pretest-posttest control group design, the data were analyzed using PASW statistics 18.0 program. The types of multimedia materials used in experimental group were science fiction movies, science documentaries, TV programs, and Power Point presentations created by students. Before and after treatment, the attitude toward science tests were administered. Pre-tests and post-test score differences between 2 groups were analyzed by ANCOVA. The differences of attitude toward science based on gender were compared by analysis of covariance. And the perception on science class with multimedia materials were also investigated. The results of this study were as follows: First, the attitude toward science was improved significantly after applying science classes using multimedia materials. Especially, there were significant difference between pre-test and post-test in the score of attitude toward science class and attitude toward science content which were sub-area of attitude toward science. Second, there was no significant difference between female and male students in total score of attitude toward science. However, the attitude toward science, scientists and society, which was a sub-area of attitude toward science, female students scored significantly higher than male students. Third, 84% student showed a positive perception that the science class enhanced their interest in science. 69% the students responded that we had thought about Science-Technology-Society. Multimedia material types which the students prefered were science fiction movie, science documentaries, science TV programs, respectively.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Korean Classical Literature and Education
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• no.32
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• pp.193-224
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• 2016

The North Korean animation is a puppet movie that is adapted The Brother and Sister Who Became the Sun and the Moon, a traditional Korean lore. The quality of this animation is acknowledged because of not only North Korea's considerably advanced animation technology but also the animation's retention of the folklore's traditional essence rather than intention to disseminate ideological propaganda. Nevertheless, the animation reveals the trasformation of its original purpose from general educative intentions for children to the educative concept of salvation by heaven is replaced by salvation by people and cultural education insteadof salvation by heaven. The appearance of the hero Jangsoe is the key adaptation of this animation, and it suggests the main principal of salvation lies in man rather than in heaven. Such adaptation complies with the requirements of children's literature suggested by the North Korea's literary history office. Furthemore the hero Jangsoe as the examplary figure of revolutionary self-reliance ideology and as a leader. Theory of self-reliance literature stipulates that children's literature is used for ideological education that develops people to be successors of revolutionary feats and become active workers for the construction of socialism and communism, therefore it is possible to understand the purpose of the adaptation to reflect the educational aims. This study investigates the change in meaning form the original folktale through such adaptation, and highlights problems related to limiting the meaning implied in "heaven's salvation" in the original story only to the vague meaning of religious hope. This vague implied meaning is considered as "an awareness activity to examine their own existence in the universe". With regard to this, the concept of heaven's salvation that is prevalent in the classic stories can be interpreted as a positive self-belief that enables the use of rationality in any helpless situation that cannot be understood with existing empirical knowledge. It considers that heaven expresses the power that exists in the human mind through self-viability and self-belief. This creates the power of reason in the character to fight against the evil disguised as the mother, in the absence of the real mother.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• The Korean Journal of Archival Studies
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• no.28
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• pp.35-81
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• 2011

Manhwa is a mass media (to expose all faces of an era such as politics, society, cultures, etc with the methodology of irony, parody, etc). Since the Manhwa records is primary culture infrastructure, it can create the high value-added industry by connecting with fancy, character, game, movie, drama, theme park, advertising business. However, due to lack of active and systematic aquisition system, as precious Manhwa manuscript is being lost every year and the contents hard to preserve such as Manhwa content in the form of electronic records are increasing, the countermeasure of Manhwa contents management is needed desperately. In this study, based on these perceptions, the need of Manhwa records management is examined, and the characteristics and the components of Manhwa records were analyzed. And at the same time, the functions of record management process reflecting the characteristics of Manhwa records were extracted by analyzing various cases of overseas Cartoon Archives. And then, the framework of record-keeping regime was segmented into each of acquisition management service areas and the general Manhwa records archiving strategy, which manages the Manhwa contents records, was established and suggested. The acquired Manhwa content records will secure the context among records and warrant the preservation of records and provide diverse access points by reflecting multi classification and multi-level descriptive element. The Manhwa records completed the intellectual arrangement will be preserved after the conservation in an environment equipped with preservation facilities or preserved using digital format in case of electronic records or when there is potential risk of damaging the records. Since the purpose of the Manhwa records is to use them, the information may be provided to diverse classes of users through the exhibition, the distribution, and the development of archival information content. Since the term of "Manhwa records" is unfamiliar yet and almost no study has been conducted in the perspective of records management, it will be the limit of this study only presenting acquisition strategy, management and service strategy of Manhwa contents and suggesting simple examples. However, if Manhwa records management strategy are possibly introduced practically to Manhwa manuscript repositories through archival approach, it will allow systematic acquisition, preservation, arrangement of Manhwa records and will contribute greatly to form a foundation for future Korean culture contents management.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.55-68
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• 2021

The purpose of this study is to analyze the characters and narratives of series films and to extract corresponding elements of film music to compare and analyze how musical elements were utilized. The scope of the study was analyzing the story structure and characters of the films "Mary Poppins" and "Mary Poppins Returns" and the corresponding film music. After comparing the contents of the film "Mary Poppins" and "Mary Poppins Returns," the film matched the film music equivalent to the similar scenes of the two films. As a result, seven of the 11 songs of "Mary Poppins" overlap with those used in similar narratives of "Mary Poppins Returns", and eight songs overlap in "Mary Poppins Returns". Seven songs from "Mary Poppins" and eight songs from "Mary Poppins Returns" can be divided into nine scenes in total when connected to a common narrative. Among them, "A Spoonful of Sugar" from "Mary Poppins", "Jolly Holiday", "A cover is not the Book" from "Mary Poppins Returns" and "Triple light fantastic" were overlapping songs with narratives. Based on this, it analyzes leitmotiv film music, focusing on characters from the films "Mary Poppins" and "Mary Poppins Returns." The common leitmotivs in the two films are Mary Poppins leitmotiv, Lesson leitmotiv, Lullaby leitmotiv, World leitmotiv, Chimney Sweeper leitmotiv, Up & Down leitmotiv, Chimney Sweeper leitmotiv, and Sky leitmotiv. The characteristic rhythm and pitch used in Mary Poppins leitmotivs were used in the overall song featuring Mary Poppins. Through this, the elements of music symbolizing Mary Poppins, a key figure, were matched to the films "Mary Poppins" and "Mary Poppins Returns" and modified according to the narrative flow. The analysis results of this work have theoretical significance in that it is necessary to analyze the narratives and film music of series films to discover common features and consider how they are matched in theoretical terms.

• Journal of Ginseng Culture
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• v.4
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• pp.103-127
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• 2022

The influx of modern culture in the early 20th century in Korea led to numerous changes in the country's ginseng industry. With the development of ginseng cultivation technology and commerce, the production and consumption of ginseng increased, and various ginseng products were developed using modern manufacturing technology. Consequently, competition for the sales of these products became fierce. At that time, newspaper advertisements showed detailed trends in the development and sales competition of ginseng products. Before 1920, however, there were few advertisements of ginseng in newspapers. This is thought to be because newspapers had not yet been generalized, and the ginseng industry had not developed that much. Ginseng advertisements started to revitalize in the early 1920s after the launch of the Korean daily newspapers Dong-A Ilbo and Chosun Ilbo. Such advertisements in this period focused on emphasizing the traditional efficacy of Oriental medicine and the mysterious effects of ginseng. There were many advertisements for products that prescribed the combination of ginseng and deer antler, indicating the great popularity of this prescription at the time. Furthermore, advertisements showed many personal experience stories about taking such products. Mail order and telemarketing sales were already widely used in the 1920s . In 1925, there were advertisements that ginseng products were delivered every day. The advertisements revealed that ginseng roots were classified more elaborately than they are now according to size and quality. Ginseng products in the 1920s did not deviate significantly from the scope of traditional Oriental medicine formulations such as liquid medicine, pill, and concentrated extract. In the 1930s, ginseng advertisements became more active. At this time, experts such as university professors and doctors in medicine or in pharmacy appeared in the advertisements. They recommended ginseng products or explained the ingredients and medicinal effects of the products. Even their experimental notes based on scientific research results appeared in the advertisements to enhance the reliability of the ginseng products. In 1931, modern tablet advertisements appeared. Ginseng products supplemented with vitamins and other specific ingredients as well as ginseng thin rice gruel for the sick appeared at this time. In 1938, ginseng advertisements became more popular, and advertisements using talents as models, such as dancer Choi Seunghee or famous movie stars, models appeared. Ginseng advertisements in the 1920s and 1930s clearly show a side of our rapidly changing society at the time.

• Korean Journal of Communication Studies
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• v.22 no.2
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• pp.89-121
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• 2014

In order to suggest a strategy for continuous growth of webtoon, this article examined webtoon usage motivation and tried to make a prediction about culture content products and services connected with webtoon, including intention for viewing movies, based on webtoon; intention for switching to paid webtoon content, and intention for buying webtoon character products. From the point of view of Uses and Gratification Theory intentions for using webtoon and human sociocultural behavior intention are already predicted but with the usefulness of Theory of Planned Behavior Integrated Model this study extended the explanation power of prediction about webtoon related behavioral intention. Results found 5 motivational factors for webtoon usage i.e. 'seeking information', 'entertainment and access availability', 'webtoon genre characteristics', 'influence from a friend or acquaintance', and 'escapism and tension release'. Among them the ones that influenced the intention for viewing movies, based on webtoon, were found to be 'webtoon genre characteristics', 'escapism and tension release' and the 3 variables from Theory of Planned Behavior. 'Seeking information', 'entertainment and access availability', 'webtoon genre characteristics', and all the 3 variables from Theory of Planned Behavior were found to influence the intention for switching to paid webtoon content. The intention for buying webtoon based character products was affected by the motivational factors 'seeking information', 'escapism and tension release' and the behavior and subjective norms variables from Theory of Planned Behavior. Based on the uncommon results from the research several suggestions were made for the continuous growth of webtoon.